Senior Associate – AI ML Engineer

We are seeking an AI/ML Engineer with 3–5 years of hands-on experience building and deploying machine learning and Generative AI applications. You will contribute across the product lifecycle—from data preparation and experimentation to API development, evaluation, deployment, monitoring, and continuous improvement. The ideal candidate combines practical AI/ML knowledge with strong software-engineering discipline and a demonstrated ability to build reliable enterprise-grade solutions across multiple industries.

- 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; select models based on quality, latency, cost, privacy, and deployment constraints.

- Implement prompt engineering, few-shot patterns, function/tool calling, structured output validation, and—where justified—fine-tuning or parameter-efficient tuning.

- 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.