Senior AI Engineer / Technical Consultant

Job summary

Responsibilities

As a Senior Technical Consultant / Senior AI Engineer, you will lead the design and delivery of impactful AI and data solutions. You’ll combine hands-on engineering with client advisory, guiding multi-disciplinary teams from opportunity framing to production deployment.

• Own solution architecture & delivery: Define end-to-end architectures (data, ML/LLM, APIs, security), make build/buy decisions, and drive projects from PoC to production with…
• Lead complex PoCs & pilots: Scope, prototype, and validate AI/ML use cases (incl. LLMs/GenAI, RAG, fine-tuning, evaluation) and convert successful pilots into scalable products.
• Advanced programming & modeling: Build, adapt, and evaluate ML/DL/LLM models using Python (PyTorch, TensorFlow, HuggingFace, LangChain/LlamaIndex), including prompt engineering…
• Cloud & MLOps/LLMOps: Design and operate solutions on Azure, GCP, or AWS (CI/CD, containers, model serving, feature stores, vector DBs, monitoring, eval pipelines).
• Client advisory & stakeholder management: Translate business goals into technical roadmaps, present to senior stakeholders, manage risks and trade-offs, and ensure measurable…

What they're looking for

• Experience: 5–8+ years in data/ML/AI engineering or adjacent roles, including end-to-end delivery of production AI systems (not just prototypes).
• Programming: Expert Python; solid software engineering (testing, packaging, CI/CD). SQL required; Java/Scala/TypeScript a plus.
• AI/ML: Hands-on with ML/DL and LLMs (prompting, fine-tuning/LoRA, embeddings/RAG, evaluation & observability, guardrails).
• Cloud: Deep experience on at least one major cloud (Azure, GCP, or AWS) deploying secure, scalable services.
• MLOps/LLMOps: Containers (Docker/Kubernetes), Git, pipelines (e.g., GitHub Actions/Cloud-native), model serving (FastAPI/TorchServe/Triton), experiment tracking, model registry…

Tech stack

• RAG
• Python
• PyTorch
• TensorFlow
• LangChain
• LlamaIndex
• Azure
• GCP