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.
Physical Demands:
Not Applicable.
Qualifications
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.
