Job Responsibilities

· Design, develop, and maintain production-grade AI applications and services using modern software engineering practices (CI/CD, testing, observability, cloud-native design).

· Define and implement foundational platforms (e.g., conversational bots, AI-powered search, unstructured data processing, GenBI) that are reusable and scalable across the enterprise.

· Lead architectural decisions, bringing best practices in software development lifecycle, explainability, and responsible AI.

· Lead cross functional team initiatives—embedded projects with business stakeholders—to rapidly build and deploy AI solutions that solve high-priority problems.

· Evaluate and integrate existing AI tools, frameworks, and APIs (e.g., LLMs, vector DBs, retrieval-augmented generation) into robust applications.

· Champion automation in workflows—from data ingestion and preprocessing to model integration and deployment. Define their success criterias, metrics and standard operation procedures.

· Partner with data scientists, product managers, and other engineers to ensure end-to-end delivery and reliability of AI products.

· Stay current with emerging AI technologies, but prioritize practical application and delivery over experimental research.

· Contribute to the internal knowledge base, tooling libraries, and documentation to scale engineering practices across the organization.

· Mentor other engineer and data scientists and provide technical leadership across projects, helping raise the bar for rigor and impact.

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Job Qualifications

Required:

· 7+ years of professional software engineering experience; ability to independently design and ship complex systems in production.

· Strong programming skills in Python (preferred), Java, or similar languages, with experience in developing microservices, APIs, and backend systems.

· Solid understanding of software architecture, cloud infrastructure (AWS, Azure, or GCP), and modern DevOps practices.

· Experience integrating machine learning models into production systems (e.g., LLMs via APIs, fine-tuning, RAG patterns, embeddings, agents and crew of agents etc.).

· Experience with large language models (LLMs), vector-based search, retrieval-augmented generation (RAG), or unstructured data processing.

· Ability to move quickly while maintaining code quality, test coverage, and operational excellence.

· Strong problem-solving skills and a bias for action, with the ability to navigate ambiguity and lead through complexity.

· Strong experience with technical mentorship and cross-team influence.

· Ability to translate complex technical ideas into clear business insights and communicate effectively with cross-functional partners.

Preferred:

· Familiarity with AI/ML tools such as LangChain, Haystack, Hugging Face, Weaviate, or similar ecosystems.

· Experience using GenAI frameworks such as LlamaIndex, Crew AI, AutoGen, or similar agentic/LLM orchestration toolkits.

· Experience building reusable modeling components or contributing to internal ML platforms.

· Background in working with embedded teams or in forward-deployed environments where rapid iteration and close business collaboration are key.

· Proficiency in Python and common ML/data science libraries (e.g., scikit-learn, pandas, NumPy, PyTorch, TensorFlow).

· Solid knowledge of machine learning fundamentals, including supervised and unsupervised learning, model evaluation, and statistical inference.

· Exposure to working with unstructured data (documents, conversations, images) and transforming it into usable structured formats.

· Experience building chatbots, search systems, or generative AI interfaces.

· Background in working within platform engineering or internal developer tools teams.