Senior Associate – AI ML Engineer

About the role

We are seeking an AI/ML Engineer with 3–5 years of hands‑on experience building and deploying machine‑learning and generative‑AI applications. The role involves contributing across the product lifecycle, from data preparation to deployment and monitoring, delivering enterprise‑grade solutions for multiple industries.

Key responsibilities

• Build and maintain end‑to‑end AI/ML and Generative AI applications, including data pipelines, model or prompt workflows, APIs, evaluation, deployment, and monitoring.
• Design Retrieval‑Augmented Generation (RAG) solutions using document ingestion, chunking, embeddings, vector search, reranking, citations, and access‑aware retrieval.
• Develop agentic AI workflows that use tools, structured outputs, state or memory, orchestration, guardrails, human‑in‑the‑loop approvals, and failure recovery.
• Integrate foundation models and AI services from commercial and open‑source ecosystems, selecting models based on quality, latency, cost, privacy, and deployment constraints.
• Implement prompt engineering, few‑shot patterns, function/tool calling, structured output validation, and fine‑tuning or parameter‑efficient tuning where justified.
• Create reproducible evaluation pipelines for accuracy, relevance, groundedness, safety, latency, reliability, and cost; maintain regression or “golden” test datasets.
• Develop production services using Python, REST APIs, asynchronous processing, and well‑defined interfaces; write clean, modular, documented, and testable code.
• Use Git and GitHub for version control, pull requests, code review, issue tracking, and release management; implement CI/CD workflows with GitHub Actions or equivalent tools.
• Containerize and deploy applications using Docker and cloud services; contribute to Kubernetes‑based deployments, autoscaling, secrets management, observability, and rollback strategies as needed.
• Apply secure AI development practices, including privacy controls, prompt‑injection defenses, authorization checks, secrets handling, content safety, auditability, and responsible AI principles.

Required profile

• Bachelor’s or Master’s degree in Computer Science, Data Science, AI, Machine Learning, Engineering or related field, or equivalent practical experience.
• 3–5 years of professional experience developing software, data, or machine‑learning solutions, with substantial hands‑on experience in Generative AI or LLM‑based applications.
• Strong Python programming skills and practical experience with libraries such as pandas, NumPy, scikit‑learn, PyTorch or TensorFlow.
• Working knowledge of LLM application patterns including prompting, embeddings, RAG, vector databases, tool/function calling, structured outputs, and agent workflows.
• Experience building and consuming REST APIs, working with JSON and schemas, and integrating databases or enterprise systems.
• Understanding of software‑engineering best practices such as modular design, unit and integration testing, logging, error handling, code review, documentation, and debugging.
• Hands‑on experience with Git/GitHub and CI/CD concepts, including automated build, test, security‑scan, and deployment workflows.
• Experience with at least one cloud platform (AWS, Azure or GCP) and containerisation using Docker; Kubernetes knowledge is a plus.
• Current, role‑relevant AWS AI/ML certification or Microsoft Azure AI certification (mandatory).
• Strong analytical, communication and collaboration skills, able to explain technical trade‑offs to both technical and non‑technical audiences.

Required skills

• Python
• pandas
• NumPy
• scikit‑learn
• PyTorch
• TensorFlow
• REST APIs
• JSON
• Git
• GitHub
• CI/CD
• AWS
• Azure
• GCP
• Docker
• Kubernetes
• LLM prompting
• Embeddings
• Retrieval‑Augmented Generation (RAG)
• Vector databases
• Tool/function calling
• Structured output validation