Role description

ML Engineer I We are seeking a highly skilled AI/ML Engineer with strong expertise in Machine Learning, Generative AI, Agentic AI, and Retrieval-Augmented Generation (RAG) to join our growing AI Practice. The ideal candidate is passionate about building production-grade AI systems and possesses hands-on experience developing intelligent agents, enterprise RAG platforms, cloud-native AI applications, and scalable machine learning solutions. You are an AI-first engineer who embraces modern AI-powered software development practices and enjoys solving complex customer challenges across multiple industries. The Opportunity As an AI/ML Engineer, you will: Design, develop, deploy, and optimize scalable Machine Learning and Generative AI solutions for enterprise customers. Build intelligent AI agents and multi-agent workflows using frameworks such as LangGraph, LangChain, Google ADK, Anthropic Agent SDK, OpenAI SDK, CrewAI, AutoGen, and Semantic Kernel. Design and implement production-grade Retrieval-Augmented Generation (RAG) systems, including data ingestion, document processing, chunking strategies, embedding generation, vector indexing, retrieval optimization, reranking, and grounded response generation. Develop robust agent orchestration workflows with advanced capabilities including tool calling, planning, state management, memory management, context engineering, workflow orchestration, observability, and evaluation. Build scalable Python backend services using FastAPI and/or Flask, exposing secure REST APIs for AI services and intelligent agents. Develop production-ready AI platforms integrating Large Language Models (LLMs), vector databases, enterprise data sources, and cloud-native AI services. Build and optimize AI pipelines for training, inference, monitoring, and continuous improvement. Fine-tune Large Language Models using modern adaptation techniques including PEFT, LoRA, instruction tuning, and domain-specific optimization where appropriate. Apply advanced Prompt Engineering and Context Engineering techniques to maximize model performance and reliability. Develop machine learning and deep learning models, including NLP and computer vision solutions, leveraging structured and unstructured enterprise data. Perform data preprocessing, feature engineering, model training, validation, evaluation, and deployment following MLOps best practices. Integrate AI models into enterprise applications using APIs, microservices, event-driven architectures, and cloud-native deployment patterns. Ensure deployed AI systems meet enterprise standards for scalability, reliability, security, governance, and Responsible AI. Continuously evaluate emerging AI technologies and recommend innovative solutions that create measurable business value. What You Need Education Masters degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Engineering, or a related field is preferred. Bachelors degree with exceptional relevant experience will also be considered. Experience 3-5 years of hands-on experience in Machine Learning, Artificial Intelligence, or Generative AI engineering. Candidates with a Masters degree and strong project experience in AI/ML are highly preferred. Proven experience delivering enterprise AI applications into production environments. Technical Skills Machine Learning & AI Strong understanding of supervised and unsupervised learning, deep learning, transformer architectures, neural networks, statistical modeling, natural language processing (NLP), and machine learning fundamentals. Experience with data preprocessing, feature engineering, model evaluation, optimization, and production deployment. Generative AI & Agentic AI Hands-on experience building AI agents using one or more of the following: LangGraph LangChain Google Agent Development Kit (ADK) Anthropic Agent SDK OpenAI SDK CrewAI AutoGen Semantic Kernel Experience designing multi-agent systems, agent orchestration, tool integration, memory management, context management, agent observability, and evaluation frameworks. Retrieval-Augmented Generation (RAG) Strong experience designing production RAG architectures. Hands-on knowledge of document ingestion, chunking strategies, embedding models, vector databases, retrieval optimization, reranking, hybrid search, grounding, evaluation, and hallucination mitigation. Experience working with vector databases such as Pinecone, FAISS, Weaviate, ChromaDB, or similar platforms. Programming & Backend Development Strong proficiency in Python. Experience developing backend services using FastAPI and/or Flask. Experience designing REST APIs and microservices following clean architecture principles. Familiarity with SQL, PostgreSQL, MySQL, Snowflake, or similar enterprise data platforms. Cloud & AI Platforms Experience with one or more cloud providers and AI platforms: AWS (Bedrock, AgentCore, SageMaker) Google Cloud (Gemini Enterprise, Vertex AI) Microsoft Azure (Azure AI Foundry, Azure OpenAI) Knowledge of cloud-native AI deployment, monitoring, scalability, and security is highly desirable. Machine Learning Frameworks Experience with: PyTorch TensorFlow Scikit-learn Hugging Face Transformers DevOps & MLOps Docker Kubernetes GitHub Actions or similar CI/CD platforms MLflow, Kubeflow, SageMaker, or other MLOps tools Monitoring, observability, and production model lifecycle management AI-First Engineering The ideal candidate embraces an AI-first software delivery model and demonstrates experience using AI-powered engineering tools such as: GitHub Copilot Cursor OpenAI Codex Claude Code Windsurf Candidates should demonstrate the ability to: Accelerate software development using AI-assisted engineering. Automate repetitive engineering tasks through intelligent tooling. Reimagine the Software Development Life Cycle (SDLC) using AI-first delivery practices. Improve engineering productivity, software quality, and development velocity through AI-driven automation. Preferred Qualifications Experience delivering AI solutions within consulting or customer-facing environments. Publications in leading AI, Machine Learning, NLP, Computer Vision, or Data Science journals and conferences are highly desirable. Experience working across industries including healthcare, insurance, retail, banking, manufacturing, and financial services. AWS, Google Cloud, Microsoft Azure, or AI platform certifications. Contributions to open-source AI projects or research communities. What We Look For Strong analytical thinking and problem-solving skills. Passion for continuous learning and emerging AI technologies. Excellent communication and stakeholder management skills. Ability to adapt quickly to multiple customer environments and business domains. Strong ownership, accountability, and customer-centric mindset. Commitment to delivering scalable, secure, production-ready AI solutions that create measurable business value.