Associate Director - Artificial Intelligence Solutions

Associate Director - AI Solutions

Roles & responsibilities 

Model Development & Solution Architecture

•Design, build, and optimize machine learning, GenAI, and statistical models to solve complex cross‑domain business challenges.•Develop reproducible, scalable workflows using modern MLOps practices; conduct rigorous model evaluation and continuous improvement for accuracy, reliability, and fairness.•Architect end‑to‑end AI systems including data pipelines, vector databases, orchestration layers, and deployment frameworks.•

GenAI, RAG & Agentic Systems

•Explore and implement advanced LLM techniques, including embeddings, retrieval‑augmented generation (RAG), agentic workflows, tool use, memory, and multimodal reasoning.•Build production‑grade GenAI applications using LangChain, LangGraph, and ecosystem tools for orchestration, evaluation, and deployment.•Experiment with foundation models across open‑source and commercial platforms to identify best‑fit models for business use cases.•

API & Integration Development

•Develop and operationalize scalable APIs—preferably with FastAPI (Python)—to integrate ML/GenAI components with enterprise systems, SaaS products, and data platforms.•Collaborate on deployment patterns including microservices, serverless, and containerized workloads (Docker/Kubernetes).•

Cloud Platforms & Infrastructure

•Architect and deploy AI solutions on Azure, Google Cloud Platform (GCP), or AWS, leveraging managed AI/ML services (Azure ML, Vertex AI, SageMaker), vector search, and serverless compute.•Ensure security, scalability, compliance, and monitoring in all cloud‑based AI deployments.•

Consulting, Problem Solving & Stakeholder Collaboration

•Partner with product teams, business stakeholders, SMEs, and engineering groups to deeply understand challenges, run discovery workshops, and shape AI strategy and roadmaps.•Translate business requirements into scalable technical architectures, solution designs, and implementation plans.•Communicate complex AI concepts in clear, business‑friendly language and drive adoption of AI‑powered solutions across the organization.

• Lead the development of agentic systems that are more than just wrappers for LLM models—systems that actively drive business outcomes with human-in-the-loop oversight

• Design and develop Multi-Agent Ecosystems where multiple specialized agents collaborate to solve complex problems using frameworks like LangGraph, or AutoGen

• Define "Agentic Thinking" and instill a mindset in the team where we build for Goals, not just Tasks. 

• Lead the adoption of standardized protocols to connect AI assistants to our internal data and tools securely.

• Take ownership of full stack application development and oversee the end-to-end SDLC of AI-native applications. 

• Incorporate human-in-the-loop design by developing UX/UI patterns wherein agents integrate seamlessly with outlined processes, operate autonomously up to a specified confidence threshold, and then gracefully hand off tasks for human approval
• Develop robust evaluation & Observability metrices to measure Agent Success Rate, Steps-to-Solution, and Cost-per-Task.
• Work closely with the business leads / designates to define workflows and be assertive enough to ensure we use the right tools for right outcomes instead of one size fits all kind of approach 
• Mentor and develop a team of AI engineers who understand vector stores, RAG, and prompt orchestration.

• 13+ Years of Experience in software engineering, with at least 4-5 years focused on AI/ML and 1-2 years specifically in Generative AI/Agentic workflows.

• Hands-on expertise with LangGraph, CrewAI, AutoGen, or LlamaIndex for multi-step reasoning and orchestration.

• Deep understanding of RAG (Retrieval-Augmented Generation), context engineering, and long-term memory design for agents.

• Strong command of Python, vector databases (Pinecone, Weaviate, etc.), and cloud AI platforms (Azure AI Foundry)

• Experience with evaluation frameworks like LangSmith or Arize Phoenix to monitor agent performance, drift, and cost.

• Background in leading AI pods or CoEs for internal tools, with skills in Java, data engineering, and cross-functional collaboration.

• Ability to articulate technical solutions to stakeholders and drive innovation in agentic systems for SDLC optimization

• Exceptional communication and interpersonal skills, with the ability to collaborate effectively with diverse, cross-functional teams.

Experience Level

Mid Level