AI & ML Services – Senior Machine Learning Engineer

Design, develop, deploy, and maintain scalable Machine Learning and Generative AI solutions that address complex business challenges. The role is responsible for building end-to-end ML systems, Agentic AI architectures, Retrieval-Augmented Generation (RAG) platforms, model serving APIs, and AI-enabled applications while partnering with business stakeholders, data scientists, product owners, and engineering teams. The Senior Machine Learning Engineer will drive MLOps best practices, model governance, cloud-native deployments, and production-grade AI solutions leveraging modern cloud and AI ecosystems.

Responsibilities

Machine Learning Engineering

• Design, build, test, deploy, and maintain Machine Learning models and AI-powered applications.
• Develop scalable feature engineering, training, validation, and inference pipelines.
• Build reusable ML frameworks and services for model deployment and monitoring.
• Translate business requirements into measurable ML use cases, success metrics, and technical solutions.
• Collaborate with Data Scientists to operationalize predictive and generative AI models.
• Optimize model performance, scalability, reliability, and cost efficiency.

Generative AI & Agentic AI

• Design and implement Agentic AI solutions using multi-agent architectures.
• Build AI agents using LangChain, LangGraph, CrewAI, AutoGen, or similar frameworks.
• Develop advanced prompt engineering strategies and orchestration workflows.
• Implement LLM-powered applications leveraging foundation models such as Gemini, GPT, Claude, or open-source alternatives.
• Design and deploy conversational AI solutions and enterprise copilots.

Retrieval-Augmented Generation (RAG)

• Design and implement end-to-end RAG architectures.
• Develop vector search solutions using embeddings and semantic search techniques.
• Integrate vector databases and enterprise knowledge repositories.
• Implement hybrid search, semantic retrieval, document chunking, ranking, and retrieval optimization.
• Evaluate and improve retrieval accuracy, grounding, and response quality.
• Fine-tune foundation models and domain-specific language models.
• Implement model evaluation frameworks and benchmarking methodologies.
• Define AI quality metrics including:
• Accuracy
• Relevance
• Groundedness
• Toxicity & Safety
• Conduct A/B testing and model comparison using industry-standard leaderboards and evaluation frameworks.
• Deploy and manage AI/ML solutions in GCP, Azure, AWS, or hybrid cloud environments.
• Implement CI/CD pipelines for Machine Learning and Generative AI workloads.
• Build automated model retraining and deployment workflows.Manage model lifecycle, versioning, governance, and auditability.
• Support production environments and ensure high availability and observability.

Required Technical Skills

Artificial Intelligence & Machine Learning

• Natural Language Processing (NLP)

Generative AI

• Agentic AI
• Prompt Engineering
• Embeddings
• Vector Databases
• AI Evaluation Frameworks

Programming Languages

• Python (Advanced)
• SQL (Advanced)
• TensorFlow
• PyTorch
• JAX
• Scikit-Learn

AI Frameworks

• LangChain
• LangGraph
• CrewAI (Preferred)
• AutoGen (Preferred)
• Amazon Web Services

Preferred Knowledge Areas

• BigQuery
• BigQuery ML
• Enterprise Data Warehousing
• Data Lake Architectures
• Spark
• Streaming Data Processing
• Batch Processing

MLOps & Automation

• Cloud Composer (Airflow)
• CI/CD for ML
• Infrastructure as Code
• Model Lifecycle Management

Containerization & Deployment

• Docker
• GKE
• REST APIs
• Model Serving Platforms

Monitoring & Observability

• Data Drift Monitoring
• Feature Drift Analysis
• Performance Monitoring
• Responsible AI Practices

Nice to Have

• Experience with Gemini ecosystem and Vertex AI Studio.
• Experience building enterprise AI copilots and agent-based workflows.
• Knowledge of Responsible AI, AI Governance, and AI Security.
• Experience with reinforcement learning and advanced AI evaluation methodologies.
• Contributions to AI/ML open-source projects.
• Exposure to multimodal AI solutions (text, image, speech, video).

Experience Level

Senior (7+ years of Data Science / Machine Learning experience)