Experience

8+ years of relevant software engineering, cloud engineering, AI/ML engineering, platform engineering, or solution engineering experience, including hands-on delivery of production-grade Generative AI, RAG, or Agentic AI systems

Key Responsibilities

• Work directly with business and technical stakeholders to identify high-value AI use cases and convert ambiguous requirements into production-ready solutions.

• Design and build Generative AI, Retrieval-Augmented Generation (RAG), Advanced RAG, and Agentic AI applications.

• Engineer retrieval pipelines covering document ingestion, parsing, chunking, metadata enrichment, embeddings, indexing, hybrid retrieval, reranking, grounding, and response generation.

• Build single-agent and multi-agent systems with planning, reasoning, tool calling, workflow orchestration, state management, memory, retries, exception handling, and human-in-the-loop approvals.

• Develop and integrate Model Context Protocol (MCP) servers and clients to securely connect AI agents with enterprise tools, APIs, data sources, and workflows.

• Build reusable tools, functions, plugins, APIs, and connectors for structured and controlled agent execution.

• Develop AI-enabled backend services and microservices using Python, FastAPI, Flask, REST APIs, asynchronous processing, and event-driven patterns.

• Integrate AI solutions with enterprise applications, databases, SaaS platforms, cloud services, security platforms, and knowledge repositories.

• Apply prompt engineering and context engineering, including system prompts, few-shot prompting, structured outputs, context compression, and conversation-memory strategies.

• Build automated evaluation pipelines to measure retrieval quality, groundedness, faithfulness, answer relevance, hallucination, task completion, latency, security, token consumption, and cost.

• Implement LLMOps and GenAIOps practices covering model and prompt versioning, testing, deployment, tracing, observability, rollback, and production monitoring.

• Implement AI security guardrails against prompt injection, indirect prompt injection, jailbreaks, sensitive-data disclosure, insecure tool execution, privilege escalation, and excessive agency.

• Rapidly develop proofs of concept and convert validated prototypes into secure, scalable, resilient, maintainable, and supportable enterprise applications.

• Troubleshoot complex AI, retrieval, application, data, API, cloud, and integration issues and drive them through root-cause analysis and closure.

• Create technical designs, architecture diagrams, evaluation reports, runbooks, deployment documentation, and operational support procedures.

• Collaborate with Product Owners, Architects, Software Engineers, DevOps, Cybersecurity, Data Engineering, and business teams while maintaining clear delivery visibility.

Required Technical Skills

Must Have

• Strong programming experience in Python, including production-grade backend and AI application development.

• Hands-on experience building Generative AI and Large Language Model (LLM) applications using Azure OpenAI, OpenAI APIs, or equivalent enterprise model platforms.

• Strong experience with RAG and Advanced RAG patterns, including query rewriting, multi-query retrieval, metadata filtering, hybrid search, reranking, contextual retrieval, citations, and grounded generation.

• Hands-on experience with Agentic AI, AI agents, tool/function calling, structured outputs, agent orchestration, and multi-agent workflows.

• Understanding of Model Context Protocol (MCP), including MCP hosts, clients, servers, tools, resources, prompts, authentication, authorization, and secure enterprise integration.

• Experience with prompt engineering and context engineering, including system instructions, few-shot examples, context-window management, memory, and output schemas.

• Strong understanding of embeddings, semantic search, vector search, similarity search, hybrid retrieval, reranking models, and retrieval-quality optimization.

• Experience with vector stores and search platforms such as Azure AI Search, PostgreSQL/pgvector, Redis, Pinecone, Qdrant, Weaviate, or equivalent.

• Experience with document ingestion and knowledge pipelines, including PDF/Office document parsing, OCR, chunking, metadata extraction, indexing, and access-control-aware retrieval.

• Experience with AI frameworks such as Microsoft Semantic Kernel, LangChain, LangGraph, AutoGen, or equivalent orchestration technologies.

• Experience building APIs and microservices using FastAPI, Flask, REST, JSON, asynchronous programming, background workers, and message queues.

• Experience with Microsoft Azure services and cloud-native application architecture.

• Experience with AI evaluation and Evals, including groundedness, faithfulness, retrieval precision/recall, answer relevance, hallucination detection, regression testing, and LLM-as-a-Judge approaches.

• Experience with LLMOps/GenAIOps, including model configuration, prompt versioning, experiment tracking, tracing, monitoring, telemetry, quality gates, and release management.

• Ability to optimize model selection, token usage, context size, latency, throughput, caching, reliability, and inference cost.

• Knowledge of Responsible AI, AI governance, privacy, data isolation, PII handling, content safety, and enterprise data-protection requirements.

• Knowledge of AI security risks, including the OWASP Top 10 for LLM Applications and controls for prompt injection, data leakage, unsafe outputs, and excessive agency.

• Experience implementing identity and access controls using OAuth 2.0, OpenID Connect, SSO, JWT, RBAC, managed identities, Key Vault, and secrets management.

• Experience with Git, GitHub, pull requests, code review, automated testing, CI/CD, GitHub Actions, or Azure DevOps.

• Experience with Docker, containers, and production deployment; Kubernetes experience is preferred.

• Strong debugging and root-cause-analysis skills across LLM, agent, RAG, application, API, cloud, data, and integration layers.

Good to Have

• Azure AI Foundry, Azure OpenAI, Azure AI Search, Azure Machine Learning, and Microsoft Semantic Kernel.

• Graph RAG, knowledge graphs, entity extraction, graph databases, and knowledge-grounded reasoning.

• Multimodal AI involving text, images, audio, video, document intelligence, OCR, and vision-language models.

• Model fine-tuning, adapters, synthetic-data generation, distillation, model routing, and small language models.

• AI observability and evaluation platforms such as Azure AI Foundry evaluations, MLflow, OpenTelemetry, LangSmith, or equivalent.

• PostgreSQL, pgvector, Redis, Cosmos DB, data lakes, event streaming, and message-broker technologies.

• Infrastructure as Code using Terraform, Bicep, or equivalent.

• Automated UI/API testing using Playwright, Pytest, contract testing, and security testing.

• GitHub Copilot and AI-assisted software development workflows.

• Experience applying AI to cybersecurity, DevSecOps, vulnerability management, threat analysis, application security, or security automation use cases.

Desired Competencies

• Strong hands-on engineering, coding, debugging, and system-design capability.

• Ability to quickly understand unfamiliar business domains, architectures, codebases, data sources, and integration constraints.

• Strong customer and stakeholder engagement with the ability to explain complex AI concepts clearly.

• Comfort operating in ambiguous, fast-moving environments while maintaining engineering discipline.

• Self-driven ownership mindset with accountability for measurable outcomes, not only activity completion.

• Ability to balance AI capability with security, reliability, performance, scalability, cost, maintainability, and business value.

• Ability to move effectively between code-level troubleshooting, architecture discussions, stakeholder discovery, and production operations.

• Ability to rapidly prototype without compromising production engineering, security, governance, and operational-readiness standards.

Weekly Hours:

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