ML Software Engineer-c2c

Job Summary

We are seeking a highly skilled and hands-on AI/ML Engineering Lead to drive the design, development, and deployment of next-generation AI solutions leveraging Generative AI, Large Language Models (LLMs), and scalable ML platforms. The ideal candidate will have deep expertise in AI/ML engineering, MLOps, cloud-native architectures, and production-grade AI systems.

This role requires strong experience building and deploying scalable machine learning and GenAI applications using Python, OpenAI APIs, RAG pipelines, vector databases, and cloud platforms such as AWS, Azure, or Google Cloud Platform. The candidate should be comfortable leading engineering teams while remaining hands-on with architecture, coding, deployment, and optimization.

Key Responsibilities

AI/ML & GenAI Engineering

• Design, develop, and deploy scalable AI/ML and Generative AI applications.
• Build production-grade LLM-powered applications using OpenAI APIs, GPT models, and modern GenAI frameworks.
• Develop Retrieval-Augmented Generation (RAG) pipelines integrating vector databases and enterprise data sources.
• Implement prompt engineering strategies, model orchestration, and evaluation frameworks.
• Fine-tune, optimize, and monitor ML/LLM models for performance, scalability, and reliability.
• Build AI agents and intelligent automation workflows.

MLOps & Platform Engineering

• Design and maintain robust MLOps pipelines for training, deployment, monitoring, and model lifecycle management.
• Automate CI/CD workflows for AI/ML applications.
• Deploy containerized ML services using Docker and Kubernetes.
• Build scalable distributed ML systems in cloud-native environments.
• Ensure observability, monitoring, governance, and security for AI platforms.

Cloud & Infrastructure

• Architect and deploy AI workloads on AWS, Azure, and/or Google Cloud Platform.
• Work with cloud AI services such as SageMaker, Azure OpenAI, Vertex AI, or equivalent platforms.
• Optimize infrastructure performance, scalability, and cost efficiency.
• Collaborate with DevOps and platform teams on infrastructure automation.

Leadership & Collaboration

• Provide technical leadership and mentorship to engineering teams.
• Drive architectural decisions and establish engineering best practices.
• Collaborate with product managers, data scientists, and business stakeholders to deliver AI-driven solutions.
• Lead technical design reviews, code reviews, and deployment strategies.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or related field.
• 8+ years of software engineering experience with strong hands-on coding expertise.
• 4+ years of experience in AI/ML engineering and production ML systems.
• Strong programming skills in Python.
• Hands-on experience with LLMs, GPT models, OpenAI APIs, and Generative AI applications.
• Experience building RAG pipelines and working with vector databases.
• Strong knowledge of MLOps tools and frameworks.
• Experience with TensorFlow, PyTorch, Scikit-learn, LangChain, LlamaIndex, or similar frameworks.
• Hands-on experience with Docker, Kubernetes, and microservices architectures.
• Strong experience with AWS, Azure, and/or Google Cloud Platform cloud platforms.
• Experience deploying scalable AI/ML systems into production environments.
• Strong understanding of REST APIs, distributed systems, and cloud-native application design.

Preferred Qualifications

• Experience with AI agents and autonomous workflows.
• Knowledge of fine-tuning and model optimization techniques.
• Experience with ML monitoring, governance, and responsible AI practices.
• Familiarity with vector databases such as Pinecone, Weaviate, ChromaDB, or FAISS.
• Experience with CI/CD pipelines and Infrastructure as Code (Terraform, CloudFormation, etc.).
• Exposure to enterprise AI transformation initiatives.

Nice to Have

• Experience leading AI engineering teams.
• Experience in enterprise-scale AI modernization initiatives.
• Knowledge of data engineering and streaming platforms.
• Exposure to multi-agent AI systems and orchestration frameworks.