vinay aluguru

As an AI Engineer, I approach challenges with a highly logical and pragmatic mindset, dedicated to crafting reliable and effective solutions. I am driven by a commitment to diligence and accountability, consistently striving for mastery in my work to deliver precise and high-quality outcomes.

Experience

AI Engineer

Turing · Mar 2025 – Jul 2026

Not yet confirmed

• Owned end-to-end delivery of RAG and multi-agent AI systems, from architecture design through production deployment on AWS.
• Led integration of multiple LLM providers (OpenAI, Claude, Gemini, Groq) into production workflows, ensuring reliable, context-aware responses at scale.
• Drove adoption of evaluation practices across projects-tracking retrieval accuracy, groundedness, latency, and hallucination rate to guide iteration.
• Integrated agentic AI frameworks and automation tools (LangChain, CrewAI, MCP, n8n) to build scalable backend services and automated workflows.
• Collaborated with cross-functional stakeholders to translate business requirements into scalable AI solutions and mentored on best practices for prompt engineering, agent orchestration, and CI/CD deployment.

AI Engineer

Turing · Feb 2025 – Jul 2026

Not yet confirmed

• • Owned end-to-end delivery of RAG and multi-agent AI systems, from architecture design through production deployment on AWS EC2.
• • Led integration of multiple LLM providers (OpenAI, Claude, Gemini, Groq) into production workflows, ensuring reliable, context-aware responses at scale.
• • Drove adoption of evaluation practices across projects—tracking retrieval accuracy, groundedness, latency, and hallucination rate to guide iteration.
• • Integrated agentic AI frameworks and automation tools (LangChain, CrewAI, MCP, n8n) to build scalable backend services and automated workflows.
• • Collaborated with cross-functional stakeholders to translate business requirements into scalable AI solutions and mentored on best practices for prompt engineering, agent orchestration, and CI/CD deployment.

LLM S2 Annotator (OpenClaw CUA Trajectory Specialist)

Turing · Nov 2024 – Jan 2025

Not yet confirmed

• Created Computer Use Agent (CUA) trajectories from natural-language instructions to align multi-step agent behavior.
• Evaluated AI-generated outputs for reasoning quality, factual accuracy, safety compliance, and strict instruction adherence.
• Performed prompt evaluation, pairwise (SxS) response ranking, and automated hallucination detection across complex outputs.
• Investigated workflow and execution failures through systematic trace analysis and tool-call validation.
• Validated multi-turn reasoning workflows, execution consistency, and context retention across multi-step execution paths.
• Collaborated with cross-functional AI research teams to develop evaluation guidelines and improve model trajectory alignment datasets.

LLM S2 Annotator (Openclaw CUA Trajectory Specialist)

Turing · Nov 2024 – Jan 2025

Not yet confirmed

• • Created Computer Use Agent (CUA) trajectories from natural-language instructions to align multi-step agent behavior.
• • Evaluated AI-generated outputs for reasoning quality, factual accuracy, safety compliance, and strict instruction adherence.
• • Performed prompt evaluation, pairwise (SxS) response ranking, and automated hallucination detection across complex outputs.
• • Investigated workflow and execution failures through systematic trace analysis and tool-call validation.
• • Validated multi-turn reasoning workflows, execution consistency, and context retention across multi-step execution paths.
• • Collaborated with cross-functional AI research teams to develop evaluation guidelines and improve model trajectory alignment datasets.

LLM Agentic Trainer

Turing · Aug 2024 – Oct 2024

Not yet confirmed

• Trained AI/LLM models using Python, SQL, Git, JSON, and TAU tools across consecutive AI engineering workflows.
• Designed and developed agentic workflows, JSON-based task structures, and tool-integration setups for multi-step execution.
• Applied prompt engineering (Zero-shot, Few-shot, Chain-of-Thought, ReAct) and RLHF-based evaluation methodologies to improve model reasoning, trajectory accuracy, and output quality.
• Evaluated multi-turn model trajectories to ensure strict criteria adherence, logical coherence, context retention, and hallucination reduction across agentic tasks.
• Developed evaluation guidelines, benchmark prompts, and documentation supporting scalable AI training and alignment datasets.

What drives their work

The Architect

You design and build robust AI systems that stand the test of real-world use.

vinay's superpower

You systematically engineer reliable AI agents, ensuring they deliver accurate, grounded, and measurable business impact.

How vinay works

You would excel in a team focused on building and maintaining robust, production-grade AI agents for enterprise or regulated industries.