Lead AI Engineer

Role:Lead Agentic AI Engineer

Experience:510 years

Employment Type:Full-time

ROLE SUMMARY

We are looking for a Lead Agentic AI Engineer who can own the end-to-end design and delivery of complex production-grade agentic systems. You will be the go-to technical expert and the engine room of our most demanding AI initiatives turning ambiguous client challenges into scalable functional platforms. You will drive technical solutioning for client engagements architect multi-agent pipelines and bridge AI engineering with business outcomes while elevating the capability of the team around you.

WHAT YOULL DO

Agentic Architecture & Engineering

• System Design:Architect multi-agent systems orchestrator/sub-agent patterns state machines tool registries using Microsoft Agent FrameworkLangGraphCrewAIAutoGen or Semantic Kernel

• Advanced RAG:Design andoptimizeretrieval pipelines: hybrid search re-ranking query expansion multi-hop reasoning and knowledge graphs

• Model Adaptation:Apply Quantization PEFT/LoRAfine-tuning and prompt optimization techniques to adapt foundation models for client-specific tasks

• Guardrails & Hallucination Control:Design and enforce comprehensive guardrail frameworks output validation factual grounding checks prompt injection defenses content filtering and hallucination-mitigation strategies (chain-of-verification retrieval grounding self-consistency) for enterprise-grade deployments

MLOps& Production Readiness

• Deployment:Productionize AI services on AWS / Azure using Docker Kubernetes and CI/CD pipelines (GitHub Actions / Azure DevOps)

• Observability:Build comprehensive monitoring for LLM systems tracking accuracy hallucinations latency cost and drift usingLangSmithorArizePhoenix

• Evaluation:Define and implement LLM evaluation suites using RAGAS G-EvalTruLens or custom metrics aligned to client KPIs

• Cost & Token Optimization:Drive down inference costs through token budgeting prompt compression KV-cache management model routing streaming strategies and intelligent batching balancing performance against cost at scale

• CI/CD:Own and evolve CI/CD pipelines for ML systems enforcing automated testing (unit contract and model-quality tests) as a standard across all engagements

• Performance Tuning:Optimizemodel serving for high-throughput production usingvLLMDeepSpeed or Triton Inference Server

Client Solutioning & Leadership

• Solutioning:Lead technical discovery and proposal for AI engagements; translate ambiguous client problems into actionable AI solutions

• Mentorship:Guide junior engineers review architecture decisions and build the teams internal library of reusable AI patterns accelerators and playbooks

• Stakeholder Communication:Present solution designs demo prototypes and communicate technical trade-offs clearly to client technical and business stakeholders

MUST-HAVE QUALIFICATIONS

• Experience:510 years in software engineering or data science with at least 3 years in applied Gen AI / LLM engineering in a services or consulting context

• Agentic Frameworks:Proven experience building production agents withLangGraphCrewAIAutoGen or Semantic Kernel

• RAG & Retrieval:Deepexpertisein RAG architectures vector databases (PineconeQdrantWeaviate) and embedding pipelines

• LLMs:Strong working knowledge of GPT-4o Claude 3.x/4.x Gemini and open-source models (Llama 3 Mistral)

• Cloud & DevOps:Hands-on with AWS / Azure AI services; Docker Kubernetes and CI/CD workflows

• Engineering:Strong PythonFastAPI SQL; software design patterns; a software engineering first approach to ML with rigorous unit integration and model-quality testing

• MLOps&Productionization:Proventrack recordtaking LLM systems from prototype to production owning deployment pipelines observability evaluation suites guardrails and ongoing model health in live client environments

• Education:/ B.E. /in Computer Science or related discipline

GOOD TO HAVE

• Fine-tuning:Experience with PEFT/LoRAfine-tuning workflows and serving optimized models

• Performance Tuning:Hands-on experience withvLLMDeepSpeed or Triton Inference Server for high-throughput model serving

• Knowledge Graphs:Exposure toGraphRAGor ontology-based retrieval strategies

• Multi-modal:Experience with vision-language models or multi-modal agent pipelines

• Certifications:AWS Solutions Architect Azure AI Engineer Associate or equivalent