Senior ML/AI Engineer

1 day ago

Bangalore, India APPIT Software Inc Full time

Design, architect, and implement state-of-the-art Generative AI applications and agentic systems using modern AI frameworks such as LangChain, LlamaIndex, or custom orchestration layers. Seamlessly integrate large language models (e.g., Build scalable, reliable, and high-performance backend systems using Python and modern frameworks to power GenAI-driven features. Take ownership of prompt engineering, tool usage, and long/short-term Deliver high-quality results rapidly by leveraging Cursor and other AI-assisted ‘vibe coding‘ environments for fast development, iteration, and debugging. Use vibe coding tools effectively to accelerate delivery, reduce development friction, and fix bugs quickly with minimal overhead. Participate in and lead the entire SDLC, from requirements analysis and architectural design to development, testing, deployment, and maintenance. Proactively identify and resolve system-level issues, performance bottlenecks, and edge cases through deep debugging and optimization. Collaborate closely with cross-functional teams—including product, design, QA, and ML—to iterate quickly and deliver production-ready features. Execute end-to-end implementations and POCs of agentic AI frameworks to validate new ideas, de-risk features, and guide strategic product development. Extensive Python Expertise: Hands-on experience in Python development, with a focus on clean, maintainable, and scalable code. ~ Software Design Principles: proven experience designing and leading complex software architectures. ~ Practical experience with frameworks like LangChain, LlamaIndex, or other agent LLM Integration: Direct experience integrating APIs from OpenAI, Anthropic, Cohere, or similar providers. ~ Strong understanding of prompt design, refinement, and optimization for LLM-based Experience architecting and implementing Retrieval Augmented Generation (RAG) pipelines Vector Databases: NLP/ML Knowledge: Solid foundation in Natural Language Processing and core machine learning concepts ~ Cloud & Deployment: Familiarity with cloud platforms like AWS, GCP, or Azure, including deployment of GenAI Proficient with Docker, with working knowledge of Kubernetes. ~ Experience with platforms such as MLflow, Weights & Biases, or equivalent tools. ~ Strong grasp of SDLC processes, Git-based version control, CI/CD pipelines, and Agile methodologies.

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