Associate - Senior AI Developer, ODEX

About Tres Vista

Tres Vista is a global enterprise whose business model is built to deliver enduring value. Tres Vista combines best practices, technology enabled execution, and industry-leading talent to drive meaningful results. By integrating advisory capabilities with scalable delivery, Tres Vista helps clients operate smarter and grow stronger. Tres Vista’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 Tres Vista, 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 seeking a Senior AI Developer to lead the design, development, and rapid prototyping of generative AI solutions. This role is between experimentation and engineering rigor.

You will not just build prototypes and MVPs but also decide how they should be built and productionized, guide juniors, and ensure solutions are scalable, reliable, and cost-effective. You are expected to have problems end-to-end and influence technical direction.

This is a hands-on builder + technical decision-maker role.

Key Role Deliverables:

- Build end-to-end MVPs and proof-of-concepts using generative AI models (text, embeddings, agents, RAG pipelines, etc.).
- Implement workflows using LLMs, prompt engineering, and agent-based architecture
- Architect LLM-based solutions using RAG, agents, tool calling, and structured outputs
- Select appropriate models, frameworks, and architecture based on use case constraints
- Own AI components end-to-end: data ingestion → reasoning → output → monitoring
- Define best practices for prompt design, versioning, evaluation, and deployment
- Make trade-offs across quality, latency, cost, and reliability
- Translate complex business problems into technical architecture and implementation plans
- Mentor junior AI developers and review their work
- Act as a go-to technical resource for generative AI questions within the team
- Stay updated on the latest in cloud platforms (AWS/GCP/Azure), software frameworks, and AI/ML technologies
- Work with commercial and open-source LLMs (e.g., OpenAI, Anthropic, Huggingface, open-source models)
- Provide input on how MVPs can be hardened for production use
- Identify failure modes (hallucinations, prompt brittleness, cost spikes) and suggest mitigations
- Contribute to building shared AI infrastructure (prompt libraries, RAG templates, agent frameworks)
- Drive reusability across projects instead of one-off solutions

Prerequisites:

- Demonstrated experience shipping LLM-powered applications to real users
- Strong production-level Python skills
- Deep hands-on experience with LLM APIs (OpenAI, Anthropic, open-source), prompt engineering at scale, Retrieval-Augmented Generation (RAG), and vector databases and embedding strategies
- Experience designing agent-based workflows and multi-step reasoning pipelines
- Strong experience building APIs and services (FastAPI preferred)
- Familiarity with docker and containerized deployments, cloud infrastructure (AWS/Azure/GCP), async processing, queues, or background workers
- Understanding of LLM observability (logging prompts, costs, outputs, failures)

- Experience with Git and collaborative development workflows

Experience:

- 3–5 years of experience in AI/ML, software engineering, or applied data science

- Experience with fine-tuning, LoRA, or model customization
- Experience building internal AI platforms or reusable frameworks
- Knowledge of security, privacy, and governance considerations for AI systems
- Exposure to enterprise or regulated environments (finance, healthcare, legal, ops-heavy domains).Prior experience in startups, innovation labs, or consulting firms a plus
- Prior experience building internal tools or automation using AI

Education:

- Bachelor’s degree in technology (B.Tech) preferably from Tier 1 institutes (IITs, NITs, BITS, etc.)

Compensation:

The compensation structure will be as per industry standards