Gen AI Engineer

Role Overview:

We are looking for a Senior AI Engineer with strong experience in Machine Learning and Generative AI. This role focuses on building and deploying reliable AI solutions that solve real business problems and operate in production environments. You will work in small teams, spending most of your time designing, building, and improving AI systems while contributing to technical decisions and collaboration across teams.

Key Responsibilities:

• Work with business and product teams to turn business needs into practical AI solutions.

• Decide when to use traditional ML techniques versus LLMs and GenAI approaches.

• Build end-to-end AI applications, including data pipelines, model integration, APIs, and automation workflows.

• Deploy scalable, secure, and reliable AI systems with monitoring, error handling, and safety controls.

• Improve performance, cost efficiency, and system reliability.

• Convert prototypes into production-ready solutions through testing and optimization.

AI & ML Expertise:

• Strong understanding of traditional ML methods and modern GenAI technologies.

• Experience building applications using LLMs such as OpenAI, Claude, Gemini, Llama, or similar models.

• Hands-on experience with RAG architectures, prompt engineering, agent-based workflows, and orchestration frameworks such as LangChain or LangGraph.

• Ability to evaluate and improve AI system quality and performance.

Technical Leadership & Collaboration:

• Serve as a senior contributor within delivery teams.

• Troubleshoot complex AI system issues and support architectural decisions.

• Partner with product, business, cloud, and platform teams.

• Follow strong software engineering and delivery practices.

Required Skills:

• 10–12 years of software engineering experience, including ML engineering.

• Strong backend development skills in Python, Java, Node.js, or similar languages.

• Experience building APIs and distributed systems.

• Hands-on experience with Docker, Kubernetes, and cloud platforms such as AWS, Azure, or GCP.

• Experience with ML frameworks such as PyTorch, TensorFlow, or scikit-learn.

• Knowledge of CI/CD, monitoring, logging, and deployment best practices.

• Strong problem-solving, communication, and collaboration skills.

Nice to Have:

• Experience with enterprise AI platforms.

• Production experience with agentic AI, multi-agent systems, and large-scale RAG solutions.

• Knowledge of AI governance, safety, and compliance.

• Experience mentoring engineers and optimizing AI performance and costs.