Manager - Senior AI/ML Engineer

Role Overview

We are seeking a Senior AI/ML Engineer with 5–9 years of experience designing, building, and operating production-grade AI and Generative AI solutions. You will provide hands-on technical leadership across solution architecture, data and model pipelines, agentic systems, evaluation, cloud deployment, and observability. The ideal candidate pairs deep AI/ML expertise with strong software-engineering discipline and sound architectural judgment and can lead delivery for complex enterprise use cases.

What You Will Do

- Own AI/ML and GenAI solutions end to end — data pipelines, model and prompt workflows, APIs, evaluation, deployment, observability, and continuous optimization.

- Design enterprise RAG platforms: secure ingestion, chunking, embeddings, hybrid/vector search, reranking, citations, and access-aware retrieval.

- Build production agentic systems using tool/function calling, structured outputs, planning and memory, multi-agent orchestration, human-in-the-loop approvals, failure recovery, and auditable traces.

- Architect and operate MCP clients and servers that expose enterprise tools, resources, and prompts — with secure transports (stdio, Streamable HTTP), authentication, least-privilege access, tenant isolation, and protection against prompt injection and unsafe tool execution.

- Integrate agents with enterprise systems (document repositories, source control, ticketing, databases, ERP/CRM, cloud services) through reusable connectors and governance patterns.

- Define evaluation strategies and quality gates for accuracy, groundedness, safety, latency, and cost, and establish end-to-end observability for agent and MCP activity.

- Build production services in Python with strong engineering practices, GitHub-based CI/CD, and cloud-native deployment on AWS or Azure using Docker, Kubernetes, and infrastructure as code.

- Partner with product, architecture, data science, security, and business stakeholders; lead design and architecture reviews and mentor engineers.

Required Qualifications

- Bachelor's or Master's in Computer Science, Data Science, AI/ML, Engineering, or a related field — or equivalent practical experience.

- 5–9 years developing production software, data, ML, or AI solutions, including hands-on delivery of GenAI/LLM applications.

- Advanced Python and practical experience with data/ML libraries (pandas, NumPy, scikit-learn, PyTorch, TensorFlow, or equivalent).

- Strong grasp of LLM and agentic architecture: prompting, context engineering, embeddings, RAG, tool/function calling, structured outputs, orchestration, evaluation, and human-in-the-loop controls.

- Proven experience designing APIs, distributed services, and event-driven or asynchronous workflows with secure tool execution for AI agents.

- Hands-on experience with Git/GitHub and CI/CD, including automated build, test, security-scan, and deployment workflows.