Job Description:

Roles and Responsibilities:

- Lead the design, development, and deployment of AI/ML solutions, Generative AI applications, and enterprise AI platforms.

- Architect and implement LLM-powered applications, RAG pipelines, AI agents, multi-agent systems, and intelligent copilots.

- Drive AI solution design, technology selection, proof of concepts, and production implementation aligned with business objectives.

- Lead and mentor AI/ML engineers, software developers, and data scientists, ensuring engineering quality and timely delivery.

- Design scalable backend services, APIs, microservices, and distributed systems to support AI-powered applications.

- Develop context engineering strategies, knowledge retrieval mechanisms, prompt engineering approaches, and model evaluation frameworks.

- Integrate AI capabilities with enterprise applications, databases, search platforms, workflow engines, and external services.

- Implement workflow automation and orchestration solutions, including human-in-the-loop processes where required.

- Establish best practices for AI/ML model development, deployment, versioning, monitoring, and continuous improvement using MLOps.

- Ensure AI solutions meet requirements for scalability, performance, security, reliability, responsible AI, and data privacy.

- Collaborate with product managers, architects, business stakeholders, and engineering teams to translate business requirements into effective AI solutions.

- Evaluate emerging AI technologies, LLMs, agent frameworks, and cloud AI services to identify opportunities for innovation and optimization.

- Drive CI/CD automation, observability, cost optimization, and operational excellence across AI/ML platforms and applications.

Skillset:

- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field.

- 8+ years of experience in software engineering, AI/ML development, or technology delivery, with proven experience leading AI/ML teams and initiatives.

- Strong programming skills in Python, with familiarity with Java, Kotlin, TypeScript, JavaScript, or Go.

- Strong understanding of Machine Learning, Deep Learning, Generative AI, Large Language Models (LLMs), and model evaluation techniques.

- Hands-on experience with Retrieval-Augmented Generation (RAG), context engineering, vector databases, embeddings, and knowledge engineering.

- Experience designing and developing AI agents, multi-agent systems, agent frameworks, and AI copilots.

- Strong understanding of backend engineering, APIs, microservices, event-driven architectures, and distributed systems.

- Experience with workflow automation, orchestration platforms, and human-in-the-loop systems.

- Knowledge of relational and NoSQL databases, caching, search platforms, analytics, and lakehouse platforms.

- Experience with cloud platforms such as AWS, Azure, or GCP, along with Docker, Kubernetes, and infrastructure-as-code.

- Strong understanding of DevSecOps, CI/CD, MLOps, model deployment, monitoring, observability, and AI security.

- Excellent leadership, communication, problem-solving, and stakeholder management skills.