Artificial Intelligence Engineer

Position Purpose:

A proficient AI Engineer will join our IT team, focusing on developing and enhancing AI Systems Engineer, you'll play a pivotal role in transitioning to an AI-driven company.

Your work will encompass designing, developing, and ship production AI solutions across ML models and LLM systems—including AI agents, RAG, Agentic AI, and Agentic RAT (Agentic RAG / Retrieval-Augmented Tooling)—using Azure, OpenAI/Azure OpenAI, and Google Gemini.

Roles and responsibilities:

AI Agents + Agentic AI (Hands-on)

Build tool-using agents that execute multi-step tasks:

planning, tool calling, verification, retries/fallbacks, and audit logs.

Implement agent orchestration (graph/state machine patterns), deterministic controls, and human-in-the-loop escalation.

RAG + Agentic RAT (Agentic RAG / Retrieval-Augmented Tooling)

Build RAG pipelines end-to-end:

ingestion, chunking, embeddings, vector/hybrid retrieval, reranking, citations, grounded responses.

Implement Agentic

RAG:

retrieval and tool-use loops ("retrieve, reason, tool-call, verify, respond") with confidence scoring

Tune retrieval quality:

metadata filters, hybrid search, prompt grounding, evaluation datasets, and regression tests.

Data Science + Machine Learning (Hands-on)

Own end-to-end ML:

problem framing, EDA, feature engineering, training, validation → deployment , monitoring.

Build ML models (classification/regression/ranking/forecasting) using scikit-learn and/or PyTorch/TensorFlow.

Apply rigorous evaluation:

cross-validation, leakage prevention, bias checks, calibration, thresholding, lift/uplift analysis.

Create production-grade feature pipelines (batch + real-time where needed) and ensure reproducibility.

ML Deployment + MLOps (Hands-on)

Deploy ML models as APIs/batch jobs (FastAPI/Azure Functions/containers) with performance and reliability.

Implement MLOps:

CI/CD for training + deployment, experiment tracking (MLflow or equivalent), model registry/versioning, rollback.

Production monitoring:

model drift, data quality checks, performance degradation alerts, latency/cost monitoring.

Write runbooks, on-call-friendly dashboards, and incident playbooks for model failures.

Cloud + Model Providers

Deploy on Azure:

Blob/ADLS, Key Vault, Azure AI Search (vector/hybrid), App Service/AKS/Functions, App Insights.

Use OpenAI/Azure OpenAI and Google Gemini with provider abstraction, prompt/version governance, and rate-limit handling.

Minimum Job Requirements (Education, Experience, Skills):

Experience with large language models like GPT-4,5, Gemini

Experience with Azure AI, Google, Gemini

Proficiency in Python and modern development environments including Git, Anaconda, PiP, Docker, and Cloud services

Ability to develop production-ready standalone libraries beyond "notebook code"

bachelor's degree in computer science, Engineering, or related field, or equivalent experience

Individual contributor mindset, with strong problem-solving and communication skills

Demonstrable previous work with LLM interfaces, sharing code repositories if applicable during the interview process

Hands-on AI agents + RAG + Agentic RAG/RAT in production (not just prototypes).

Strong Python engineering + proven delivery of production systems.

Hands-on DS/ML: built models, validated them rigorously, and deployed them.

Hands-on MLOps: pipelines, versioning, monitoring, drift detection, rollback.

Strong communication:

can explain tradeoffs, risks, and decisions clearly.

Physical Demands:

Not Applicable.

Working Conditions and Environment:

Incumbent works in a temperature-controlled office environment. Incumbent sits at a desk during regularly scheduled work hours; answers and makes telephone calls using a standard telephone; types on a standard keyboard; reads and comprehends information from a computer terminal and/or written resources.