AI/ML Engineer - 6 Months Contract

Overview

Ai And Ml Engineer

Mlops & Deployment Ownership

• Define and oversee
• MLOps practices
• including:
• Agent and model versioning
• Evaluation tracking
• Deployment gating and promotion workflows
• Rollback and recovery procedures
• Collaborate with internal stakeholders and
• external delivery teams
• to ensure reliable production deployments.

Evaluation, Monitoring & Observability

• Own the
• evaluation framework
• for:
• LLM‑based agents
• RAG pipelines

• Vision Language Models (VLMs)

• Voice AI models (OpenAI Whisper, Chatterbox, Vibe Voice, or equivalent)
• Define and maintain:
• Offline evaluation methodologies
• Online monitoring and regression detection thresholds
• Human‑in‑the‑loop review processes
• Set up and manage
• AI observability tooling
• (e.g., Langfuse or equivalent) across all environments.

Performance Reporting & Insights

• Build and maintain
• product performance reporting
• , covering:
• Model accuracy and agent effectiveness
• Latency and cost‑per‑interaction
• Bias, quality trends, and stability across markets
• Provide clear technical insights to
• non‑technical stakeholders
• .

Rlhf & Continuous Improvement

• Design and oversee

Rlhf (Reinforcement Learning From Human Feedback)

• pipelines:
• Data collection and feedback ingestion
• Annotation guidelines and reward criteria
• Feedback loops for continuous improvement
• Direct implementation by external teams and
• monitor quality improvements over time
• .

Agent Memory Systems

• Own the design and validation of
• agent memory architectures
• , including:
• Short‑term context windows
• Long‑term retrieval
• Episodic memory across sessions
• Memory lifecycle policies (retention, expiry, cost control)
• Define test criteria to ensure consistency across deployment environments.

Model Benchmarking & Optimization

• Evaluate and benchmark
• VLMs and voice models
• under constrained infrastructure.
• Recommend optimization strategies:
• Quantization
• Distillation
• Runtime and model selection per jurisdiction
• Validate production readiness in
• on‑prem or sovereign environments
• .

Production Readiness & Rollouts

• Oversee production deployments executed by vendor teams.
• Run final validation checks and
• sign off on production readiness
• .
• Document deployment patterns, baselines, and environment‑specific configurations to accelerate future market rollouts.

Privacy & Data Residency

• Evaluate and recommend
• privacy‑preserving deployment patterns
• , including:
• On‑device inference
• Data isolation
• Local or sovereign model hosting
• Ensure compliance with jurisdictional data residency requirements.

Technical Requirements

• **3–5 years**
• of experience in applied AI, LLMOps, MLOps, or similar technical AI roles.
• Strong

Python

• expertise:
• Type hints, async programming, FastAPI
• Code reviews, evaluation scripts, prototyping pipelines
• Experience with LLM application patterns:
• RAG pipelines
• Prompt engineering
• Multi‑agent orchestration
• Solid background in
• supervised ML
• (scikit‑learn, XGBoost, LightGBM, or equivalent).
• Strong understanding of
• MLOps fundamentals
• :
• Model versioning
• Experiment tracking
• CI/CD deployment pipelines
• Monitoring and rollback strategies
• Hands‑on experience with:
• RLHF or human‑feedback‑driven improvement loops
• LLM/VLM/voice AI evaluation frameworks
• Agent memory architectures
• Working knowledge of:

• Vision Language Models (VLMs)

• Voice AI systems across latency, language, and hosting constraints
• Understanding of
• model optimisation techniques
• (quantization, distillation, ONNX).
• Experience using
• AI observability tools
• (Langfuse, LangSmith, or equivalent).
• Comfortable directing or overseeing
• external/vendor engineering teams
• .
• Ability to work independently in ambiguous and non‑standard infrastructure environments.
• Good to Have
• Experience with
• sovereign cloud
• or government‑regulated infrastructure.
• Familiarity with
• agentic AI frameworks
• (LangChain/LangGraph, CrewAI, PydanticAI).
• Exposure to
• federated learning
• or privacy‑preserving inference.
• Background in
• healthcare, insurance, or regulated domains
• .
• Experience building
• performance dashboards
• for non‑technical audiences.