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
Knowledge, Skills And Abilities
Education
- Bachelor's degree or master's in computer science, Engineering, or related technical discipline.
Experience
- 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.
Other Requirements (licenses, Certifications, Specialized Training – If Required)
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.
