Job Responsibilities

·Architect and deliver AI-driven solutions that address high‑value business needs, including document interpretation, automated decision flows, feedback generation, dashboard creation, data reconciliation, and approval management.

·Lead the development of agent-based workflows using LLMs, retrieval pipelines, and multi‑agent orchestration frameworks.

·Build Composite AI architectures, combining language models, search, business rules, embeddings, and analytics.

·Proven experience designing, developing, and deploying Generative AI (GenAI) solutions using large language models (LLMs) such as GPT, Llama, Claude, etc

·Design and optimize context pipelines—chunking strategies, prompting structure, memory systems, and vector retrieval mechanisms.

·Develop robust backend components and API integrations supporting AI agents, Azure AI services, and MCP-based tools.

·Experience with modern GenAI frameworks and libraries (e.g., LangChain, LlamaIndex, Hugging Face Transformers).

·Evaluate new use cases, propose viable AI solutions, and guide stakeholders through feasibility and solution design.

·Ensure solutions align with responsible AI standards, quality benchmarks, and enterprise governance requirements.

·Collaborate with architects, product managers, and business teams to align AI initiatives with enterprise goals.

·Monitor system performance, optimize agent behaviors, and evolve solutions post-deployment.

·Extensive experience with Microsoft Azure services and cloud architecture patterns

·Deep understanding of CI/CD pipelines, automated testing, and DevOps practices

·Experience with microservices architecture, API design, and distributed systems

·Experience mentoring engineers and building high-performing teams

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Knowledge, Skills and Abilities

Education

·Bachelor’s degree or master’s in computer science, Engineering, or related technical discipline.

·3–5+ years of AI engineering experience, ideally within large organizations or enterprise platforms.

·Proven track record of leading AI or GenAI initiatives, from concept to deployment.

Knowledge and skills (general and technical)

Strong command of:

·Large Language Models and modern GenAI techniques

·Agentic design principles (tool‑augmented agents, planners, multi-agent coordination)

·Retrieval-based systems (embeddings, vector search, context assembly)

·Proficiency in Python and experience developing scalable backend components and APIs.

·Hands-on expertise with Azure AI / Azure OpenAI, Azure Functions, APIM, storage, and related cloud services.

·Experience building solutions that go beyond simple automation tools—focusing instead on intelligent, adaptive systems.

·Ability to collaborate directly with business partners, understand real workflows, and translate them into AI-powered solutions.

Experience with advanced Agentic frameworks:

·LangChain, LangGraph, Azure AI Agents

·Multi-agent routing, tool-calling patterns, DAG-based orchestration

·Hands-on experience with vector databases (Azure AI Search, Pinecone, FAISS).

·Exposure to MCP (Model Context Protocol) and enterprise tool-chains integrating LLM agents with backend systems.

·Prior involvement in building solutions for domains such as:

·Operational decision flows

·Document-heavy processes

·KPI/insights generation

·Approval or workflow automation

·Familiarity with cloud-native architecture, DevOps pipelines, and observability tooling.

·Experience with AI developer tools: GitHub Copilot, OpenAI’s code assistants, or similar systems.

·Understanding of enterprise data protection, compliance considerations, and responsible AI practices.