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AI Implementation Specialist Department: Technology / Operations Reports To: IT HEAD/ COO/CEO Employment Type: Full-Time ________________________________________ About the Role We are looking for a curious, tech-savvy, and proactive AI Implementation Specialist to help drive the company's AI and automation initiatives. This is a hands-on, execution-focused role where you will work closely with different teams to identify manual, repetitive workflows and transform them using AI-powered tools and automation. No team of your own to manage — just real impact through real projects. This is a great opportunity for someone early in their career who is passionate about AI and wants to be at the forefront of how businesses use it day-to-day. ________________________________________ Key Responsibilities AI Project Support & Execution • Assist in the planning, coordination, and execution of AI projects from kick-off to deployment. • Research and evaluate AI tools, platforms, and solutions that fit specific business needs. • Work with vendors and internal stakeholders to integrate AI tools into existing systems and workflows. • Document project progress, timelines, and outcomes to keep stakeholders informed. Workflow Automation • Conduct walkthroughs with department teams (Finance, Purchasing, Operations, Sales, Logistics, etc.) to understand and map out manual processes. • Identify tasks suitable for automation and propose practical AI-driven solutions. • Build and deploy automations using no-code/low-code tools such as Make (Integromat), Zapier, Microsoft Power Automate, or similar platforms. • Monitor and maintain deployed automations, troubleshooting issues and making improvements over time. AI Tools Adoption • Help onboard employees onto new AI tools through simple guides, walkthroughs, and hands-on support. • Act as an internal resource and first point of contact for questions related to AI tools in use. • Stay up to date on new AI tools and use cases, and share relevant findings with the team. Reporting & Documentation • Track key metrics such as time saved, error reduction, and adoption rates for implemented solutions. • Maintain clear documentation of all automations, integrations, and AI workflows. • Prepare simple reports and updates for management on project status and results. ________________________________________ Requirements Must-Have • 1–3 years of experience in a tech, operations, or process improvement role. • Hands-on experience with at least one automation or no-code tool (e.g., Zapier, Make, Power Automate, Notion AI, etc.). • Comfortable using and exploring AI tools (e.g., ChatGPT, Claude, Gemini, or similar). • Strong attention to detail and ability to document processes clearly. • Good communication skills — able to explain tech concepts simply to non-technical colleagues. • Self-starter attitude with a willingness to learn and figure things out. Nice-to-Have • Exposure to basic scripting or programming (Python, JavaScript) — not required but a plus. • Familiarity with project management tools (Asana, Jira, Notion, Trello). • Experience working across multiple departments or in a cross-functional environment. • Understanding of prompt engineering or working with LLM-based tools. ________________________________________ Success Metrics • Number of manual workflows successfully automated within the first 6–12 months. • Time savings and efficiency gains reported by teams using automated processes. • Employee adoption and satisfaction with AI tools introduced. • Quality and completeness of process documentation maintained. ________________________________________

Our client is a global institutional investor managing capital at scale across public and private markets. The mandate for this team is simple: turn data into edge, and edge into decisions.These are not back-office support roles. You will sit inside the investment and research workflow. Your code, models, and pipelines will touch live capital allocation, risk, and alpha generation. The bar is high because the data is proprietary and the users are portfolio managers, not analysts.We are hiring across three tracks. You can apply to one or multiple if your profile spans them.Note: All roles are Individual Contributor positions. Our client is hiring across multiple levels — from strong mid-level to Principal/Staff1. Data Scientist – Investment ResearchMissionBuild and deploy models that extract signal from structured and unstructured data to inform discretionary and systematic investment decisions.ScopePartner directly with sector PMs and researchers to frame ambiguous problems as testable hypothesesDevelop ML/NLP/time-series models on alternative data, market data, and fundamental datasetsProductionize research: your notebook isn’t the deliverable — the monitored, versioned model isExplain trade-offs to non-technical stakeholders. “The AUC is 0.82” is not an answer.Profile We’re Calibrating ForExperience quant research, data science, or ML engineering with direct exposure to financial marketsStrong Python + SQL. Experience with scientific stack: pandas, scikit-learn, PyTorch/TensorFlow, statsmodelsComfort with messy, real-world data. You’ve joined 12 tables to build one feature and lived to tell about itMSc/PhD in STEM, Stats, CS, Financial Engineering preferred, but we’ll take a killer GitHub over a logoYou think in base rates and Bayesian updates, not just in accuracy scores2. Data Engineer – Investment PlatformMissionDesign and run the data backbone that feeds research, backtesting, and production trading. Latency, lineage, and correctness are your P&L.ScopeBuild and maintain high-throughput pipelines for market, reference, alternative, and fundamental dataArchitect storage + compute: think petabyte-scale, cloud-native, but cost-awareImplement data quality, observability, and entitlement frameworks. Bad data here loses real moneyEnable quants and DS to self-serve without creating a ticket or waiting 3 daysProfile We’re Calibrating For4-10 years building production data systems, ideally in finance, trading, or high-stakes analyticsPython/Scala/Java + SQL. Spark, Airflow, Kafka, dbt, Snowflake/BigQuery/DatabricksDeep understanding of partitioning, versioning, and replay. “It works on my laptop” is a fireable offenseYou design for failure. Your pipelines have DLQs, retries, and alerts that actually wake someone upBonus: experience with market data, tick data, point-in-time correctness, corporate actions3. Quantitative Developer – Systematic StrategiesMissionSit between research and production. Take prototype strategies and make them run fast, clean, and safely with real capital.ScopeTranslate researcher code into low-latency, testable, production-grade systems — C++/Python/RustBuild sim/backtest frameworks that match production behavior. No “research vs prod” driftOwn tooling for signal generation, portfolio construction, risk checks, and execution hooksProfile, optimize, and harden. When the market opens, your code doesn’t get a second chanceProfile We’re Calibrating For3-10 years as a quant dev, low-latency dev, or SWE in trading/systematic fundsStrong C++ or Python with performance intuition. You know why virtual functions hurt and when to use them anywayLinux, git, CI/CD, numerical computing. You’ve debugged a race condition at 2am before a US openUndergrad/MS in CS, Math, Physics, Engineering. Competitive programming/ICPC background is a plusYou respect researchers but you don’t ship their for-loops to prodShared DNA Across All Three RolesOur client hires for three traits regardless of track:Ownership – You ship, you support, you fix. No “not my system” here.Rigor – Financial data has no undo button. Test it, document it, prove it.Low ego, high output – The PM is your customer. Your job is to make them better, not to publish papers.Hiring ProcessTypically 3 rounds: Technical deep-dive → System/research design → CIO/Head of Platform.We move in days, not months. Take-home tests are rare; live problem solving is common. Show more Show less

OverviewAs an AI Engineer supporting Security Operations Center (SOC), you will contribute to the development, implementation, and enhancement of AI‑driven and automated security solutions. You will work closely with senior SOC engineers, automation specialists, and SOC analysts to help improve incident detection, response efficiency, and operational workflows using automation and AI technologies.This role focuses on hands‑on development, support, and optimization rather than full platform ownership. You will gain exposure to real‑world SOC environments, automation platforms, and applied AI while building a strong foundation in security operations and AI engineering.ResponsibilitiesOversee the configuration, maintenance, and optimization of our Automation and AI technologies to ensure their effectiveness in automating and orchestrating security workflows.Design, develop, and implement automated workflows and playbooks within Automation and AI technologies to streamline incident response processes and enhance SOC efficiency.Integrate Automation and AI technologies with existing SOC tools and technologies, orchestrating workflows across disparate security systems for seamless response coordination.Develop and deploy automated response actions and playbooks to enable rapid and effective response to security incidents, minimizing manual intervention and reducing response timesIntegrate threat intelligence feeds and indicators of compromise (IOCs) into the Automation platform to enrich incident context and enable automated threat detection and response.Monitor the performance of the Automation and AI technologies, identifying and addressing any issues or bottlenecks to ensure optimal functionality and reliability.Provide technical support during security incidents, leveraging Automation and AI technologies to automate response actions and facilitate incident investigation and resolution.Collaborate with other SOC engineering teams and stakeholders to share knowledge, insights, and best practices related to Automation and AI operations. Foster a culture of continuous learning and improvement within the SOC.QualificationsSkills : -Experience using APIs and integrating security tools.Introductory experience with cloud platforms (AWS, Azure, or GCP).Understanding of security frameworks and standards (e.g., MITRE ATT&CK).Proficiency in workflow development and playbook creation within Automation platforms.Strong understanding of Large Language Models, Machine Learning algorithms, security technologies, protocols, and industry best practices.Excellent problem-solving, analytical, and communication skills.Ability to work effectively in a fast-paced, dynamic environment and manage multiple priorities simultaneously.1–3 years of experience in: SOC operations, cybersecurity engineering, automation, or AI / data engineering roles with exposure to security use casesInternship or project-based experience in SOC automation or AI is acceptable.Bachelor’s Degree in Computer Science, Information Technology, Cybersecurity, AI, or a related field. Show more Show less

About Joveo:Every company says they're "AI-first." We actually are. Joveo's recruitment advertising platform processes millions of hiring decisions through machine learning, real-time bidding, and predictive analytics — helping the world's largest employers find the right people, faster and fairer. But we're not done. Not even close.Role: Machine Learning EngineerLocation: RemoteRole Overview:We are hiring a Machine Learning Engineer to build the models and infrastructure that power Joveo's AI-driven hiring decisions. You will own the full ML lifecycle — from feature engineering to model deployment — ensuring our models are accurate, scalable, and production-ready.Key Responsibilities:- Design, train, and deploy machine learning models at production scale- Build feature engineering pipelines and maintain feature stores- Develop model evaluation frameworks and monitor production model performance- Collaborate with data scientists to productionize experimental models- Build and maintain ML infrastructure — training pipelines, serving layers, and registries- Research and evaluate new ML techniques applicable to recruitment advertisingRequired Skills & Qualifications:- Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow, or XGBoost)- Experience with end-to-end ML pipelines from data ingestion to model serving- Familiarity with ML platforms (SageMaker, Vertex AI, MLflow, or Kubeflow)- Solid understanding of supervised, unsupervised, and reinforcement learning fundamentals- Experience with feature stores, model versioning, and experiment tracking- Strong software engineering skills — writing clean, tested, production-grade ML codeEqual Opportunity Employer:Joveo is an equal opportunity employer. We are committed to building an inclusive workplace and welcome applications from all qualified individuals regardless of race, color, ethnicity, nationality, gender, gender identity or expression, sexual orientation, age, religion, disability, marital status, or any other characteristic protected by applicable law. All hiring decisions are made solely on the basis of qualifications, skills, and demonstrated ability.If your dream job is one that doesn't fit neatly into a job title — apply.Joveo. Where AI meets the future of work. Show more Show less

Job PurposeTo lead and implement AI/ML initiatives across the Bank’s technology landscape, aligning strategic objectives with data-driven insights, while ensuring regulatory compliance, operational efficiency, and innovation through the use of machine learning and advanced analytics.Minimum Qualification: Master’s Degree/PhD in Computer Science, Econometrics, Statistics, or related fields.ExperienceMinimum 10+ years in AI/ML or data science, with at least 5 years in a leadership role.Proven experience in banking or financial services is highly preferred.Hands-on with AI/ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) and tools (Python, SQL, Spark).Experience in Azure ML tools: Databricks etc.Experience with end-to-end model lifecycle management and MLOps. Show more Show less

DeepLight AI is a specialist consultancy implementing intelligent enterprise systems, with deep expertise in financial services and banking. We are seeking a Generative AI Engineer to join our team, focusing on turning advanced research into high-impact, production-ready enterprise solutions.You won't just train models; you will design, build, and own the entire lifecycle of AI systems that leverage Large Language Models (LLMs), RAG, and Multimodal AI to solve our clients' most complex problems.The Challenge: Owning the LLM Production PipelineThis role demands a unique combination of applied machine learning, data engineering, and scalable software delivery, operating entirely within a robust MLOps/LLMOps framework .🛠️ What You Will Be Doing (Key Responsibilities):Design & Deliver GenAI Solutions: Leading the implementation of LLM-based applications, including custom chatbots, advanced summarization services, and creative co-pilotsModel Optimization & Steering: Applying cutting-edge techniques like fine-tuning, LoRA/PEFT, and RLHF to optimize model performance and ensure factual accuracyRAG System Architecture: Architecting and building high-performance Retrieval-Augmented Generation (RAG) pipelines, managing the full lifecycle of embeddings and context-aware retrievalProduction Code: Writing robust, deployment-ready software in Python and TypeScript, delivering clean, modular codeMLOps Automation: Implementing full LLMOps/MLOps using Docker, Kubernetes, and Terraform to automate CI/CD, deployment, and versioning across major cloud platforms (AWS/GCP/Azure)Data Grounding: Ensuring model intelligence is fresh and reliable by connecting to Airflow, dbt, and Kafka pipelinesGovernance & Safety: Embedding responsible AI practices, including hallucination control, bias mitigation, and auditability, into every deployed systemRequirementsDeep knowledge of transformers, diffusion models, and advanced prompt engineeringProven expertise in embeddings, vector databases, and designing context-aware retrieval pipelinesProficiency in Python, REST APIs, containerization, CI/CD, and model versioningProficiency in Python and TypeScript; core ML development using PyTorch, TensorFlow, and the Hugging Face ecosystemExpertise with LangChain, LlamaIndex, and APIs from OpenAI/AnthropicUse of MLflow and Weights & Biases for experiment tracking and versioning, leveraging platforms like Vertex AI or SageMakerMastery of Docker, Kubernetes, and Terraform for scalable deployment across major clouds (AWS, GCP, Azure)Familiarity with Airflow, dbt, Postgres, and Kafka for data ingestion and transformationExperience & Qualifications3-7 years in AI/ML, with a critical 1-3 years hands-on with LLMs or production GenAI applicationsProven delivery of production-grade GenAI tools in agile, rapid-iteration environmentsBSc/MSc in Computer Science, Data Science, or a related technical fieldExperience with model evaluation, data privacy, and bias mitigationHands-on experience with ChromaDB, Pinecone, Weaviate, and FAISS for retrieval and embedding managementBenefitsBenefits & Growth Opportunities: Competitive salary Comprehensive health insurance Ongoing professional development Opportunity to work on cutting-edge AI projects Flexible working arrangements Career advancement opportunities in a rapidly growing AI companyThis position offers a unique opportunity to shape the future of AI implementation while working with a talented team of professionals at the forefront of technological innovation. The successful candidate will play a crucial role in driving our company's success in delivering transformative AI solutions to our clients.Please note: If you fail to meet the required criteria in the screening questions, your application will not be progressed Show more Show less