GenAI / AI-ML Engineer

Role overview

GenAI / AI-ML Engineer at CloudThat

Translate business problems into measurable AI/ML and Generative AI objectives, solution approaches and evaluation criteria.

Role summary We are seeking an experienced GenAI / AI-ML Engineer to design, build, deploy and operate production-grade AI/ML and Generative AI solutions on AWS.

The role also requires a strong understanding of LLM token consumption, model pricing and AWS service/infrastructure costing, with the ability to design solutions that balance business requirements, technical performance and operational cost.

Responsibilities

• Design and implement production-grade LLM, RAG, conversational AI and agentic AI solutions on AWS.
• Build RAG pipelines including document ingestion, chunking, embeddings, metadata filtering, vector retrieval, reranking and generation.
• Design and implement Amazon Bedrock Agents and Bedrock AgentCore-based agentic solutions, including tool use, memory/state, runtime execution and orchestration as applicable.
• Work with Amazon Bedrock foundation models and evaluate models based on quality, latency, token consumption and cost.
• Build integrations between LLMs/agents and enterprise APIs, databases, applications, tools and knowledge sources.
• Develop and evaluate supervised, unsupervised and deep-learning models based on business requirements.
• Apply appropriate machine-learning algorithms for classification, regression, clustering, anomaly detection and other relevant use cases.
• Perform data preprocessing, feature engineering, model training, validation, hyperparameter tuning and error analysis.
• Use Amazon SageMaker for appropriate ML development, training, experimentation, deployment and model lifecycle requirements.
• Establish evaluation mechanisms for LLM response quality, retrieval quality, groundedness, hallucination, latency and cost.
• Analyze and optimize LLM input/output token consumption, context size and model selection to control GenAI costs.
• Estimate and optimize AWS infrastructure and service costs across model inference, compute, storage, APIs, vector search and other components.
• Design end-to-end AWS architectures using appropriate managed AI/ML, compute, data, integration and security services.
• Implement security, access control, logging, monitoring, observability and operational controls for production AI systems.
• Deploy and operate AI/ML solutions on AWS and troubleshoot latency, scalability, reliability, model quality, infrastructure and cost issues.
• Monitor deployed ML models for performance, drift, reliability and business-aligned metrics.
• Collaborate with data engineers, software engineers, architects and product teams to build reliable production solutions.
• Produce reusable components, technical documentation, architecture decisions and implementation guidance for delivery teams.
• Required qualifications and experience
• 5+ years of relevant experience in AI/ML engineering, machine learning, Generative AI, applied data science or software engineering.
• Strong hands-on Python development experience with good understanding of object-oriented design, API integration, debugging and production engineering.
• Demonstrable experience taking AI/ML or GenAI solutions from POC/experimentation through production deployment.
• Mandatory hands-on AWS experience implementing and deploying AI/ML or GenAI solutions.
• Strong hands-on experience with Amazon Bedrock and foundation models.
• Hands-on experience with Amazon Bedrock AgentCore and/or Bedrock Agents for agentic AI solutions.
• Hands-on experience with Amazon SageMaker for ML model development, training, deployment or lifecycle management.
• Experience with AWS services supporting GenAI architectures, including Amazon S3, Amazon OpenSearch Service/Serverless, AWS Lambda, API Gateway, Step Functions, CloudWatch, IAM, KMS and Secrets Manager.
• Strong understanding of LLMs, embeddings, vector search, prompt/context engineering and RAG.
• Working experience with agentic AI, tool/function calling, agent orchestration and state management.
• Strong understanding of conventional machine-learning algorithms, model evaluation and statistical reasoning.
• Ability to select appropriate algorithms, models and AWS services based on the business problem.
• Strong understanding of trade-offs between model quality, accuracy, latency, scalability, reliability and cost.
• Understanding of LLM token economics, including input/output tokens, context consumption and model pricing.
• Ability to understand and estimate AWS service/infrastructure costs for an AI/ML solution.
• Ability to translate business requirements into secure, scalable, maintainable and cost-effective AWS AI/ML architectures.
• Mandatory Skills Python, Pandas, NumPy, Scikit-learn Conventional ML – Classification, Regression, Clustering, Anomaly Detection Feature Engineering, Model Training & Evaluation Cross-validation, Hyperparameter Tuning, Error Analysis AWS – Bedrock, SageMaker, Knowledge Bases, OpenSearch/OpenSearch Serverless RAG, Embeddings, Vector Retrieval Agentic Workflows, Tool/Function Calling, State Management LLM Evaluation, Groundedness, Hallucination Mitigation LLM Token Consumption & Cost Estimation AWS Service/Infrastructure Cost Estimation & Optimization Good-to-Have Skills Docker, CI/CD, Kubernetes/EKS MLflow / SageMaker MLflow PyTorch/TensorFlow Advanced MLOps & Model Monitoring Fine-tuning, PEFT, LoRA, QLoRA Advanced NLP, Computer Vision, Forecasting Bedrock Agents / AgentCore Multimodal GenAI Voice AI / Conversational AI GraphRAG / Knowledge Graphs Bedrock Guardrails / Evaluation MCP / A2A & Agent Interoperability Advanced LLM Observability & Evaluation Advanced Multi-Agent Architectures

Employment details

• Location: Bangalore - Head Office [India]

Additional role details

• The role requires strong Python engineering, solid understanding of conventional machine-learning algorithms, practical experience with LLMs, RAG and agentic AI, and strong hands-on expertise with AWS AI/ML and Generative AI services.
• The engineer will be responsible for taking AI solutions from problem definition and solution design through development, evaluation, production deployment and optimization.
• The candidate should be capable of making architecture and technology decisions based on scalability, latency, reliability, security, model quality and cost.