AI Engineer

Key Skills: Prompt Engineering, Agentic AI, Application Development, LLM, Python, Containerization (Docker, Kubernetes), Gen AI, Api, ReactJS, Pytorch, Typescript, Tensorflow, ML

Roles and Responsibilities:

• Build and ship production-ready AI/ML features--from data ingestion and feature engineering to model training, evaluation, and deployment.
• Develop LLM/GenAI solutions (prompt engineering, tool use, guardrails) and RAG pipelines (chunking, embeddings, vector search, caching, re-ranking).
• Optimise training and inference performance via batching, quantisation, distillation, LoRA/PEFT, accelerator utilisation (GPU/TPU), and efficient memory/latency tuning.
• Build and maintain MLOps/LLMOps workflows--CI/CD for models and prompts, model registry/versioning, feature stores, and automated promotion across environments.
• Instrument observability for data, models, and prompts (telemetry, metrics, traces, dashboards, alerts); implement A/B tests and online/offline evaluation.
• Embed Responsible AI considerations (fairness, explainability, safety, bias testing) and document assumptions, datasets, and limitations.
• Document architecture, workflows, and best practices to support scalability and ongoing maintainability.
• Conduct code reviews, write unit/integration/e2e tests (including data and prompt tests), and uphold engineering standards and documentation.
• Work with advanced AI/ML frameworks, cloud services, and container orchestration platforms.
• As an AI Engineer, you are responsible for designing, building, and deploying scalable AI and machine learning solutions that solve real-world business problems, partnering closely with data scientists to productionize models and integrate them seamlessly into applications and enterprise workflows

Skills Required:

• Hands-on experience with GenAI, Gemini or Open source LLMs , Train , finetune and Onboard new LLMs
• Experience in building GenAI applications using Python
• Hands-on Experience with API Development and Microservices architecture and End to End integrations
• Knowledge of RAG (Retrieval-Augmented Generation ) and ADK, MCP
• Solid understanding of LLMs, prompt engineering, and graph-based workflows.
• Hands-on Experience with API Development and Microservices architecture
• Experience in CI/CD pipelines, and containerization (Docker/Kubernetes)., Harness and Git actions.
• Practical experience implementing LLM and GenAI solutions, including prompt engineering, model fine-tuning, RAG pipelines, embeddings, and vector databases.
• Build scalable data pipelines and workflows on GCP (Big Query, Vertex AI, Dataflow, Pub/Sub, Redis and NoSQL Databases , Maintaining chat history etc.
• Optimize model performance, monitor production systems, and ensure reliability , Auto Scaling using Prometheus, Dynatrace and Lang Smith

Desirable skills/knowledge/experience: (As applicable)

• Strong hands-on experience building and deploying machine learning models, including preprocessing, feature engineering, training, evaluation, and optimisation.
• Knowledge of API Gateways and ISTIO , ability to Diagnose and intercept failures in End to End communication.
• Implement best practices for data governance, security, and MLOps on GCP.
• Proficiency with Python and common AI/ML frameworks such as TensorFlow, PyTorch, JAX, scikit-learn, and Hugging Face libraries.
• Knowledge of MLOps and LLMOps practices--including CI/CD for models, model registry/versioning, feature stores, orchestration, and automated deployments.
• Ensure AI solutions meet security, privacy, compliance, and responsible AI standards.
• Understanding of secure engineering and data protection practices, including IAM, secrets management, encryption, and safe handling of sensitive data.
• Ability to optimise performance of training and inference pipelines--profiling, quantisation, distillation, batching, caching, or hardware acceleration.
• Collaborate with data scientists to productionize models and integrate them into applications, workflows, and APIs.

Education: Bachelor's or Master's degree in Engineering