Machine Learning Engineer (AI/ML & Generative AI)

We are looking for a hands-on Machine Learning Engineer to design, develop, deploy, and scale AI/ML systems, with a strong focus on Large Language Models (LLMs), Generative AI, and production-grade AI applications.

The ideal candidate will have strong Python engineering skills and experience building AI applications using modern frameworks and infrastructure. This role is suited for someone comfortable working in a fast-paced, high-ownership environment where requirements may evolve quickly and engineers are expected to take broad ownership from experimentation through production deployment.

You will work on advanced AI systems involving LLMs, multi-agent architectures, intelligent automation, and real-world business workflows.

Requirements

Key Responsibilities

• Design, develop, and deploy production-grade machine learning and Generative AI applications.
• Build and integrate LLM-powered applications, intelligent agents, and AI automation workflows.
• Develop scalable backend services and APIs using Python and FastAPI.
• Work with modern ML frameworks and technologies to develop, evaluate, and improve AI systems.
• Design and implement AI/ML pipelines covering experimentation, evaluation, deployment, monitoring, and optimization.
• Integrate foundation models and LLM APIs into production applications.
• Build reliable AI systems capable of handling complex, multi-step workflows.
• Work with cloud infrastructure and containerized environments to deploy and scale ML applications.
• Collaborate with engineering and product teams to translate business problems into practical AI solutions.
• Evaluate model performance, identify failure modes, and continuously improve accuracy, reliability, latency, and cost.
• Contribute to technical architecture decisions across ML systems, APIs, infrastructure, and deployment.
• Work effectively in ambiguous environments and take ownership across the complete development lifecycle.

Technical Requirements

• Strong proficiency in Python and experience building production software.
• Strong understanding of Large Language Models (LLMs) and Generative AI.
• Hands-on experience with FastAPI or similar Python-based backend frameworks.
• Experience building and deploying production AI/ML applications.
• Understanding of machine learning fundamentals, model development, evaluation, and deployment.
• Experience working with APIs, data pipelines, and scalable backend systems.
• Strong software engineering practices, including testing, debugging, version control, and production deployment.

Infrastructure & ML Stack

• Experience with Kubernetes and containerized application deployment.
• Experience with Google Cloud Platform (GCP) or comparable cloud environments.
• Experience with PyTorch or other modern deep learning frameworks.
• Familiarity with production ML infrastructure, monitoring, and deployment practices is preferred.

Experience

• 3–5 years of relevant professional experience in Machine Learning, AI Engineering, Software Engineering, or a closely related field.
• Demonstrated experience taking AI/ML solutions from experimentation or prototype through production.
• Experience working on LLM, GenAI, agentic AI, or intelligent automation systems is strongly preferred.

Candidate Profile

• Comfortable working in an early-stage or high-growth environment with broad ownership.
• Strong problem-solving and analytical abilities.
• Able to operate effectively with ambiguity and changing requirements.
• Strong communication and cross-functional collaboration skills.
• Demonstrated ability to take ownership of technical problems and deliver production-ready solutions.
• Founding engineer or startup experience is preferred.
• Experience contributing to published research or open-source LLM/agent projects is a strong plus.
• Healthcare or healthcare-AI domain exposure is beneficial but not mandatory.

Education

A Bachelor's degree in Computer Science, Engineering, Machine Learning, Artificial Intelligence, or a related discipline is preferred.

Equivalent practical experience, strong production engineering experience, significant open-source contributions, research work, or startup/founding experience may also be considered.

Candidate Preferences

• Gender: No preference.
• Notice Period: Candidates with a notice period of 30–45 days or less are preferred.
• Current Industry: No specific current-industry requirement. Candidates from AI, ML, software engineering, SaaS, technology, research, or other relevant domains are welcome.

Additional Preferred Criteria

• Founding engineer or startup background with demonstrated ability to take broad ownership.
• Published research, technical publications, or meaningful open-source contributions in LLMs, GenAI, agents, or machine learning.
• Experience working on complex AI workflows or multi-agent systems.
• Exposure to healthcare or other highly regulated domains is an advantage.

Must-Have Skills

• Python
• Large Language Models (LLMs)
• FastAPI
• Machine Learning
• Generative AI

Good-to-Have Skills

• Kubernetes
• GCP
• PyTorch
• LLM/Agent Frameworks
• Production ML Deployment
• MLOps
• Open-Source AI/ML Contributions
• Healthcare AI