Job description

Job Description: ML & Agentic AI Engineering - Lead Programmer Analyst (Exp: 5 Yrs to 7 Yrs)

Build and deliver solutions that combine predictive machine learning with LLM applications and autonomous workflows. Own implementation from data preparation and model evaluation through deployment, while mentoring junior engineers and maintaining engineering quality.

Required experience and technical skills:

5+ years of professional experience in software engineering, data science or ML engineering, including hands-on delivery of production ML solutions and practical LLM/agentic AI applications.
Strong Python and SQL; proficiency with data preparation and ML libraries such as pandas, NumPy, scikit-learn and XGBoost or equivalent tools.
Solid understanding of statistics, feature engineering, class imbalance, cross-validation, data leakage, overfitting and model explainability.
Experience with LLM APIs, prompt and context design, structured outputs, embeddings, vector search, RAG and tool/function calling.
Experience with LangChain or equivalent frameworks for LLM integration and stateful agents with approvals, retries and execution limits.
Experience serving models through FastAPI or Flask; working knowledge of Git, automated tests, containers and a cloud platform.
Ability to explain model trade-offs to stakeholders and diagnose data, model and application failures.

Key responsibilities:

Translate business problems into measurable ML and AI outcomes; establish baselines, success metrics and delivery plans.
Prepare datasets, engineer features, and train classification, regression, forecasting or anomaly-detection models appropriate to the use case.
Build RAG applications and agents that retrieve knowledge, call business APIs and use predictive model outputs in workflows.
Implement model validation, experiment tracking, reproducible pipelines, prediction services, monitoring and retraining processes.
Evaluate retrieval, generated answers and agent task completion; implement permissions, approvals, failure recovery and cost controls.
Review designs and code, guide juniors through implementation and debugging, and take ownership of delivery quality.

Mentoring and technical ownership:

Demonstrated ability to groom junior engineers through pairing, code reviews, practical assignments and actionable feedback.
Break work into clear tasks, define acceptance criteria, review progress and help juniors become independent contributors.
Create reusable components and implementation guides; communicate risks and coordinate with product, data and delivery teams.

Preferred skills:

Exposure to CrewAI / LangGraph for agent orchestration and multi-agent workflows.
Deep learning with PyTorch or TensorFlow/Keras; Hugging Face Transformers for pretrained models and adaptation.
MLflow for experiments and model tracking; LlamaIndex for RAG; pgvector, Qdrant or equivalent vector stores.
Time-series modelling, open-model serving, enterprise integrations and sensitive-data handling. Frameworks listed are alternatives; mastery of every tool is not required.

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