QUALIFICATIONS
Education: Bachelor’s or Master’s in CS, AI/ML, Data Science or equivalent practical experience
Experience: 5–8 years in software engineering; 3+ years building production-grade automation solutions
Demonstrated end-to-end delivery of agentic AI systems or complex enterprise RPA prototypes
Certifications (Preferred): AWS, Azure or GCP; RPA platforms such as UiPath or Automation Anywhere
Scope: Leads solution architecture independently, owns LLM adaptation strategy end-to-end, mentors junior engineers; leads stakeholder discovery workshops
SKILLS
Agentic AI: LangChain/LangGraph, AutoGen, CrewAI
Agent patterns: tool use, memory, multi-agent coordination, guardrails, failure recovery
LLM Fine-Tuning & Adaptation: LoRA/QLoRA with HuggingFace PEFT or Unsloth; Dataset prep, evaluation benchmarking, model versioning; Serving fine-tuned models:vLLM,GPTQ, GGUF
RAG & Vector Infrastructure: Pinecone, Weaviate, Qdrant; embeddings, retrieval evaluation
RPA: UiPath, Automation Anywhere, Power Automate in production
Engineering: Python (production quality); Cloud AI services (Bedrock,Azure, OpenAI, Vertex AI)
RESPONSIBILITIES
Agent Design & Engineering:
Architect multi-agent systems with branching logic, exception handling & human-in-the-loop escalation.
Define agent tool integrations, memory, context management & state persistence.
LLM Adaptation Strategy:
Own fine-tuning strategy (fine-tune vs RAG vs prompt engineering) and deliver end-to-end
Manage GPU training runs, model merging, quantization & production serving
RPA & HYBRID AUTOMATION:
Production & Operations:
Build agent evaluation frameworks; implement observability & tracing (LangSmith, Arize)
CI/CD for agent/model deployments; diagnose hallucination, tool misuse & cost runaway
Stakeholder & Leadership:
