Lead AI Engineer

About TresVistaTresVista is a global enterprise whose business model is built to deliver enduring value. TresVista combines best practices, technology enabled execution, and industry-leading talent to drive meaningful results. By integrating advisory capabilities with scalable delivery, TresVista helps clients operate smarter and grow stronger. TresVista’s services include investment diligence, industry research, valuation, fund administration, accounting, and data analytics.About Department:The Operational and Digital Excellence (ODEX) team is a newly established, cross-functional task force within TresVista, focused on driving firm-wide transformation. Our mandate is to reimagine and elevate the way we operate across internal support functions and client-facing teams through process optimization, technology enablement, and digital innovation.ODEX sits at the intersection of strategy, operations, and technology. The team leads high-impact initiatives that improve efficiency, scalability, and client delivery. This includes applying principles of process re-engineering, automation, and data-driven decision-making, as well as exploring the responsible and practical use of AI.It is a highly selective and agile team made up of high-performing individuals who bring a deep understanding of our business, high integrity, willingness to learn, and a strong appetite for change. Joining ODEX means stepping into a visible, high-impact role with the opportunity to shape the future of our operations and contribute to firm-wide excellence.Role Overview:We are looking for a Lead / Staff AI Engineer to own the architecture, standards, and long-term direction of our generative AI systems. This role sits above day-to-day feature delivery and below pure management.You will design systems that scale across teams, ensure technical excellence, and turn generative AI from experimentation into a reliable, reusable capability for the organization. You will be responsible for building intelligent agents from the ground up including prompt design, retrieval pipelines, fine-tuning models, and deploying them in a secure, scalable cloud environment. You’ll also implement caching strategies, handle backend integration, and prototype user interfaces for internal and client testing.This role requires deep technical skills, autonomy, and a passion for bringing applied AI solutions into real-world use. This is a high-impact individual contributor role with significant technical authority.Key Role Deliverables:Define and own the end-to-end architecture for generative AI systems across multiple use cases and teamsEstablish and enforce standards for RAG, agent architectures, prompt and version management, evaluation, observability, and deploymentDecide when to build, buy, fine-tune, or replace models, tools, and frameworks based on technical and business constraintsDesign, evolve, and govern shared AI platforms, including reusable RAG pipelines, agent orchestration frameworks, prompt management systems, and evaluation/monitoring infrastructureDrive reuse and standardization, eliminating one-off AI solutions and reducing long-term technical debtArchitect complex AI workflows, including multi-agent systems, tool orchestration, and long-running or asynchronous tasksDesign AI systems resilient to hallucinations, noisy inputs, partial failures, and model degradationOptimize AI systems for latency, cost, reliability, scalability, and explainability at production scaleLead technical design reviews, act as a technical authority, and unblock complex architectural and implementation challengesMentor and raise the technical bar for senior and junior engineers across the generative AI stackDefine and enforce guardrails for data security, privacy, compliance, and responsible AI usageProactively identify model risks, operational failure modes, and scaling bottlenecksTranslate long-term business and product goals into concrete, extensible AI platform capabilitiesDesign, build, and optimize retrieval-augmented generation (RAG) pipelines using vector databases (e.g., Qdrant, Pinecone, FAISS) to power semantic search and intelligent document workflowsFine-tune and adapt LLMs using Hugging Face Transformers, LoRA/PEFT, DeepSpeed, or Accelerate where domain adaptation is requiredIntegrate knowledge graphs (e.g., Neo4j, AWS Neptune) into agent pipelines for enhanced context, reasoning, and relationship modelingImplement cache-augmented generation strategies (semantic caching, Redis, vector similarity) to reduce latency, cost, and output inconsistencyBuild and maintain scalable backend services using FastAPI or Flask and support lightweight user interfaces or prototypes using Streamlit, Gradio, or React when neededMonitor and evaluate model and agent performance using prompt testing, benchmarks, human-in-the-loop feedback, observability tools, and safe AI practicesStay current with advancements in cloud platforms (AWS/GCP/Azure), LLMs, agentic frameworks, and AI infrastructure, and incorporate improvements where appropriatePrerequisites:Strong Python development skills, including API development and service integrationProven track record of designing and scaling AI systems used by real teams or clientsExpert-level Python and strong software engineering fundamentalsDeep, hands-on expertise with LLM APIs and open-source models, RAG architecture and vector search strategies, agent-based systems and tool calling and prompt engineering at scaleExperience with model fine-tuning, adapters, or hybrid architectureStrong background in distributed systems and API design, Docker, CI/CD, and cloud infrastructure, and async workflows, queues, and background processingExperience implementing observability for AI systems (metrics, logs, tracing, cost monitoring)Experience:6–10 years of experience in AI/ML, with at least 2 years focused on large language models, applied NLP, or agent-based systemsDemonstrated ability to build and ship real-world AI-powered applications or platforms, preferably involving agents or LLM-centric workflowsStrong analytical, problem-solving, and communication skillsAbility to work independently in a fast-moving, collaborative, and cross-functional environmentPrior experience in startups, innovation labs, or consulting firms is a plusExperience with AI governance, model audits, and compliance frameworks is a plusEducation:Bachelor’s degree in technology (B.Tech) or Master of Computer Applications (MCA),MS in similar field preferred

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