AI Engineer (6 months contract)

Overview

Full-Time

Role Overview

Key Responsibilities

• Design and deliver end-to-end LLM applications: scope the use case, build, evaluate, deploy, and hand over reliable, observable services.
• Build and optimize RAG pipelines: data parsing, ingestion, chunking, embeddings, vector search, re-ranking, and context-window optimization.
• Design and orchestrate agentic workflows for reasoning, planning, and task execution.
• Build evaluation and observability: define metrics (accuracy, latency, cost-per-task), run eval pipelines, and monitor systems in production.
• Integrate AI components into client applications via secure backend APIs; collaborate with data, software, and DevSecOps teams.
• Engineer the supporting data layer: pipelines, feature/embedding stores, and data quality for AI workloads on cloud platforms.
• Manage the full lifecycle: deployment, monitoring, versioning, rollback readiness, and cost/performance optimization.
• Translate business problems into AI solutions and communicate model capabilities, limitations, and trade-offs clearly to clients and stakeholders.
• Maintain technical documentation, runbooks, and delivery artifacts across Design & Align → Build → UAT → Production & Hypercare.

Candidate Requirements

• Demonstrated experience delivering AI solutions in an enterprise or client-facing/consulting environment.
• Prior experience at management consulting firms and/or Big Tech is an advantage.
• Strong Python and solid software engineering fundamentals.
• Hands-on LLM APIs (Anthropic, OpenAI, Azure OpenAI), including tokenization, latency, and cost control.
• Proven RAG architecture experience with vector databases (e.g., Azure AI Search, Qdrant, Chroma, Pinecone, FAISS).
• Experience with agent/orchestration frameworks (e.g., LangChain, LangGraph, CrewAI).
• Evaluation and observability skills for production AI (eval pipelines, LLM-as-judge, monitoring).
• Working knowledge of a major cloud AI/data stack (Azure, AWS, or GCP) and MLOps tooling (e.g., MLflow, Docker).
• Solid data engineering skills: SQL, distributed processing (e.g., Spark/Databricks).
• Strong collaboration and communication skills — able to work with both technical teams and senior client stakeholders.

Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field.
• 3–6 years of experience building and shipping AI/ML solutions into production.
• Preferred certifications (nice to have): cloud AI certifications (Azure AI Engineer, AWS ML, Databricks GenAI Engineer).