Job Summary
Hands-on AI Consultant to design and operationalize domain SLM, RAG / GraphRAG, and multi-turn agentic solutions—from enterprise data ingestion and fine-tuning to low-latency serving, APIs, security, observability, HA/DR, and governance.
Key Responsibilities
Skill Requirements
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Core Area |
Key Technologies & Techniques |
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Model Engineering |
Python, PyTorch, Transformers, Hugging Face, TRL/PEFT, SFT, LoRA/QLoRA/DoRA, DPO/RLHF, BF16/FP16, DDP/FSDP/ DeepSpeed ZeRO, MLflow / W&B. |
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RAG & GraphRAG |
Chunking, embeddings, hybrid search, reranking, Qdrant, Neo4j/AuraDB, Amazon Neptune, etc.; ontology, entity resolution, Cypher/Gremlin, vector-graph retrieval, grounding and citations. |
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Inference & APIs |
vLLM, TensorRT-LLM, SGLang, TGI/Triton, batching, KV/prefix cache, speculative decoding, quantization, FastAPI / OpenAI-compatible APIs, structured output and tool calling; intelligent model routing using rules, semantic/complexity classifiers, cascades, cost-quality-latency policies, FinOps budgets, metering, fallback, and routing observability. |
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Platform & Resilience |
Docker, Kubernetes, Helm, Terraform, CI/CD, Azure/AWS/GCP, multi-zone HA, cross-region DR, autoscaling, failover, backup/PITR, observability, RTO/RPO. |
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Security & Quality |
Layered guardrails, prompt-injection defense, PII/secrets, RBAC/ACL, HITL, auditability, OpenTelemetry tracing, evaluation, drift monitoring, latency/throughput/cost SLOs. |
Other Requirements
Experience & Qualifications
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8-10 years in software, data, platform, or AI engineering; 5+ years in AI/ML and 4+ years in GenAI, SLM, or RAG.
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Hands-on ownership of a production domain model or enterprise RAG platform, with strong architecture, stakeholder, and cross-functional leadership.
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Bachelor’s or Master’s degree in Computer science / AI&ML / Data Science / Engineering, or related discipline.
Preferred Candidate Profile
Hands-on architect who combines deep model, retrieval, platform, security, and operations expertise to move enterprise GenAI from experimentation to governed, highly available production.