AI Engineer

Architect and Implement AI Solutions

• Design and build RAG pipelines using Azure AI/Search and vector databases: chunking, embeddings, hybrid/semantic ranking, re-ranking, evaluation, and citation display.
• Build enterprise conversational systems (multi-turn, retrieval-grounded) with prompt lifecycle management, guardrails, audit logging, and telemetry.
• Support multiple LLMs and modalities: Azure OpenAI, Llama (Meta), Claude, etc.., and task-specific OSS models (vision, speech), with policy-driven model routing for performance, safety, and cost.

Integrate and Operate AI Infrastructure

• Implement Model Context Protocol (MCP) servers integrating with project related areas.
• Provide tool functions with RBAC scopes, schema versioning, rate limiting, request/response validation, and audit trails.
• Deploy Azure AI Agent Service (AGA) patterns for agent registry/broker/governance with agent telemetry and policy enforcement.
• Use Azure Batch for large-scale, parallel inferencing/vectorization jobs; leverage AWS EMR for distributed data/feature processing in AI pipelines.

Develop and Manage Data Pipelines

• Build ingestion and enrichment for RAG connectors and ETL/ELT: document normalization, PII redaction, metadata enrichment, SLA/SLO monitoring, and lineage.
• Operate large-scale vectorization with quality gates and drift monitoring.
• Use Azure Data Factory (ADF) and Azure Databricks for orchestrated, scalable data processing; use AWS EMR for Hadoop/Spark workloads supporting AI features.

Build Agentic AI Solutions

• Design secure tool-calling and multi-agent orchestration using Semantic Kernel, AutoGen, Microsoft Agent Framework, CrewAI, Agno, and LangChain or others.
• Know how to apply agent governance and MCP-based controls across heterogeneous agents and runtimes (register, observe, govern, retire).

Model Evaluation and Optimization

• Evaluate and fine-tune open-source and proprietary models; optimize for quality, latency, safety, and cost with A/B and offline eval suites.
• Implement CI/CD with automated tests, security scans. Have knowledge on how to secure model workloads.

Software Engineering Emphasis (Core)

• CS fundamentals: algorithms, data structures, complexity, distributed systems, networking, concurrency.
• SDLC excellence: clean architecture, design patterns, SOLID principles, unit/integration/e2e tests, testing pyramids.
• Secure coding & threat modelling for AI apps: input validation, sandboxed tool functions, secrets hygiene, role-based access & least privilege.
• Performance engineering: profiling, caching, vector index tuning, latency/throughput optimization, and cost controls (token/embedding/compute).
• Collaboration & Delivery: Agile ceremonies, RACI clarity, cross-functional delivery with product/design/data/security.

Knowledge Requirements Cloud AI Tech Stack (Azure & AWS)

• Azure: Azure OpenAI; Azure AI/Search; Azure Machine Learning; Azure Kubernetes Service (AKS); Azure Functions; Azure API Management; Key Vault; Event Hub; App Insights; Log Analytics; Azure Batch; Azure Data Factory (ADF); Azure Databricks.
• AWS: Amazon SageMaker; AWS Bedrock; Amazon Kendra; Amazon Comprehend; AWS Lambda; Amazon API Gateway; AWS Secrets Manager; Amazon S3; Amazon CloudWatch; Elastic Kubernetes Service (EKS); Amazon EMR.
• Vector DBs & Indexing: Azure AI Search vector storage, Redis, FAISS/HNSW; hybrid search + semantic ranking.
• Frameworks: Semantic Kernel, AutoGen, Microsoft Agent Framework, CrewAI, Agno, LangChain.
• Local/Edge Inference: running models locally via Docker/Ollama/vLLM/Triton; GPU provisioning; quantization (GGUF) for Llama-family models., * LangChain, Hugging Face, MLflow; Kubernetes + GPU scheduling; vector search tuning (HNSW/IVF).
• Responsible AI: policy mapping, red-team playbooks, incident response for AI.
• Hybrid/multi-cloud deployments using Azure Arc and AWS Outposts; CI/CD for AI workloads across Azure DevOps and AWS CodePipeline. Mindlance is an Equal Opportunity Employer and does not discriminate in employment on the basis of Minority/Gender/Disability/Religion/LGBTQI/Age/Veterans.

• Education: Bachelor s degree in Computer Science, Engineering, Information Technology, Data Science or equivalent hands-on expertise.
• Experience: 6+ years of software engineering experience, with at least 2+ years in applied LLM/GenAI (RAG, agents, eval, safety).

Certification Requirements:

Mandatory:

• Microsoft Certified: Azure AI Fundamentals (AI-900)
• Microsoft Certified: Azure Data Fundamentals (DP-900)
• Responsible AI certifications
• AWS Machine Learning Specialty
• TensorFlow Developer
• Kubernetes CKA/CKAD
• SAFe Agile Software Engineering (ASE), * GenAI architecture mastery: RAG, vector DBs, embeddings, transformer internals, multi-modal pipelines.
• Agentic systems: Azure AI Agent Service patterns, MCP servers, registry/broker/governance, secure tool-calling.
• Languages: C# and Python (production-grade), .Net, plus TypeScript for service/UI when needed.
• Azure & AWS services (see Knowledge Requirements) with hands-on implementation and operations.
• Model ops: eval suites, safety tooling, fine-tuning, guardrails, traceability.
• Business & delivery: solution architecture, stakeholder alignment, roadmap planning, measurable impact.