Role Overview :

As an Applied AI Engineer based in Hyderabad, you will sit at the intersection of cutting-edge research and scalable production engineering. You will be responsible for architecting and deploying sophisticated agentic workflows that transform how our business interacts with unstructured data. Working closely with cross-functional product teams, data scientists, and infrastructure engineers, you will bridge the gap between experimental LLM prototypes and robust, enterprise-grade AI solutions. Your work will directly influence our operational efficiency and customer experience by building intelligent systems that automate complex reasoning tasks, ensuring our AI initiatives deliver measurable business value and maintain a competitive edge in the market.

Key Responsibilities :

- Design and implement complex multi-agent systems using LangGraph to automate high-stakes decision-making processes for our internal stakeholders.

- Architect scalable Agentic RAG pipelines that leverage the Model Context Protocol (MCP) to ensure seamless data integration across disparate enterprise silos.

- Orchestrate LLM workflows by optimizing prompt engineering strategies and inference paths to maximize accuracy and minimize latency for end-users.

- Integrate AI services into Kubernetes-based production environments, ensuring high availability and performance for mission-critical applications.

- Embed DevSecOps principles into the AI lifecycle to maintain rigorous security standards and compliance while accelerating deployment cycles.

- Monitor and improve engineering velocity and system reliability by tracking DORA metrics to ensure our AI infrastructure remains resilient and agile.

Required Skillset :

- Demonstrated expertise in building production-ready AI applications using Python, with a deep understanding of LLM orchestration frameworks and agentic design patterns.

- Proven ability to translate ambiguous business requirements into technical specifications, communicating complex AI concepts effectively to non-technical stakeholders.

- Strong background in container orchestration and cloud-native development, specifically managing AI workloads within Kubernetes clusters.

- Proficiency in implementing DevSecOps best practices, with a focus on automating testing, security scanning, and CI/CD pipelines to improve DORA metrics.

- A degree in Computer Science, Engineering, or a related quantitative field, supported by 5 - 15 years of hands-on experience in software engineering and applied AI.