Key Responsibilities
· Lead the end-to-end deployment of GenAI applications for customers—from discovery to delivery.
· Architect and implement robust, scalable solutions using Python, Langchain/LangGraph, and LLM frameworks.
· Act as a trusted technical advisor to customers, understanding their needs and crafting tailored AI solutions.
· Collaborate closely with product, ML, and engineering teams to influence roadmap and core platform capabilities.
· Write clean, maintainable code and build reusable modules to streamline future deployments.
· Operate across cloud platforms (AWS, Azure, Google Cloud Platform) to ensure secure, performant infrastructure.
· Continuously improve deployment tools, pipelines, and methodologies to reduce time-to-value.
Required Qualifications
· 5–8+ years of experience in software engineering or solutions engineering, ideally in a customer-facing capacity.
· Proven expertise in Python, Langchain, LangGraph, and SQL.
· Deep experience with engineering architecture, including APIs, microservices, and event-driven systems.
· Demonstrated success in designing and deploying GenAI applications into production environments.
· Strong proficiency with cloud services such as AWS, Google Cloud Platform, and/or Azure.
· Excellent communication skills, with the ability to translate technical complexity to customer-facing narratives.
· Comfortable working autonomously and managing multiple deployment tracks in parallel.
Preferred Qualifications
· Familiarity with CI/CD, infrastructure-as-code (Terraform, Pulumi), and container orchestration (Docker, Kubernetes).
· Background in LLM fine-tuning, retrieval-augmented generation (RAG), or AI/ML operations.
· Previous experience in a startup, consulting, or fast-paced customer-obsessed environment.
Education
· Bachelors, Master's, or Ph.D. in Computer Science, Engineering, or a related technical field.
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