Sr Specialist Software Engineering (AI Forward Deployed Engineer)

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 pro