Senior AI Engineer developing and launching AI products for Valsoft Corporation. Collaborating on AI initiative across portfolio companies with hands-on software engineering tasks.
Responsibilities
Drive the development and launch of new AI-powered products — from concept and prototype to market-ready release — with a focus on rapid iteration and measurable impact.
Operate with a startup mindset: bias toward action, ownership across the full stack, and the ability to turn ambiguity into execution.
Build scalable systems that not only attract new customers but also deepen adoption and upsell opportunities across our existing base.
Build RESTful APIs, microservices, front and back-end components using a polyglot approach (Python, JavaScript frameworks)
Develop context strategies (e.g., embeddings, retrieval augmentation, windowing) for reliability, security, and cost efficiency
Apply evaluation frameworks (Promptfoo, OpenAI Evals, LangSmith) to systematically test, compare, and validate LLM outputs
Build and tune AI models, assistants, agentic workflows, vector databases.
Work closely with senior engineers and product owners to clarify requirements and deliver solutions that add measurable business value
Work closely with other teams to design interfaces and get them implemented for consumption by AI tooling and products.
Mentor engineers on prompt design, evaluation best practices, and context strategies
Participate in sprint planning, code reviews, and backlog grooming.
Integrate regression and adversarial testing into CI/CD pipelines to prevent prompt drift
Define automated evaluation suites for LLM applications (truthfulness, consistency, compliance)
Support efforts to reduce technical debt and improve maintainability.
Experiment with and apply Generative AI and ML tools where they can accelerate development or improve user experience
Create, optimize, and manage complex prompt strategies to maximize AI model performance
Develop reusable prompt templates for consistent outputs across multiple use cases
Design and optimize NLQ interfaces that let users query enterprise data (SQL/NoSQL, APIs, vector stores) in natural language, applying context engineering and evaluation frameworks to ensure accuracy, security, and business relevance.
Troubleshoot and optimize performance, scalability, and data-handling issues
Adapt to different technical environments across the portfolio.
Requirements
5–8 years of professional software engineering experience with Python and JavaScript frameworks
Hands-on expertise with Generative AI and ML tools (Promptfoo, OpenAI Evals, LangSmith, LangChain, AWS Bedrock, Azure AI, etc.) is a must
Familiarity with vector databases (e.g., PostgreSQL/pgvector, Pinecone, Weaviate, Milvus) and RAG (Retrieval-Augmented Generation) pipelines is preferred
Deep and recent experience with Generative AI or machine learning development tools (Claude Code, Github Copilot, AWS Bedrock, OpenAI, LangChain, Azure AI, etc.) is preferred
Proven ability to design and implement NLQ solutions enabling users to query structured/unstructured data sources conversationally, with focus on accuracy, performance, and security.
Familiarity with Git-based workflows and CI/CD pipelines.
Collaborative mindset with strong communication skills
Comfortable working in a remote, distributed environment
Willingness to learn, experiment, and take initiative.
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