Senior AI Developer

About Gruve

Gruve is an innovative software services startup dedicated to transforming enterprises to AI powerhouses. We specialize in cybersecurity, customer experience, cloud infrastructure, and advanced technologies such as Large Language Models (LLMs). Our mission is to assist our customers in their business strategies utilizing their data to make more intelligent decisions. As a well-funded early-stage startup, Gruve offers a dynamic environment with strong customer and partner networks.

Position Summary

We are hiring a Senior Developer for AI Development & Engineering to design, build, and operate production AI systems end to end. You will work across the full AI stack: classical machine learning, deep learning, and modern generative AI (LLMs, RAG, and agentic workflows). You will turn ambiguous business problems into AI capabilities our users trust, taking ownership from data through deployment, monitoring, and iteration.

Our developers work fluidly across AI-assisted coding (Claude Code, Cursor) and traditional manual coding, deploying onto Azure with Git-based workflows and CI/CD. We expect strong software engineering instincts, comfort with experimentation, and a bias toward making AI systems observable, evaluable, and safe in production.

Candidates are highly preferred from Indian Institutes of Technology (IITs) for this position.

Key Responsibilities

• Design, train, evaluate, and ship machine learning and deep learning models (classification, regression, ranking, vision, NLP, time series) against well-defined business and scientific problems.
• Design and implement LLM-powered applications using major model providers (Claude/Anthropic, Azure OpenAI, OpenAI, or equivalents).
• Build retrieval-augmented generation (RAG) systems, including chunking strategies, embeddings, vector store selection (e.g., Azure AI Search, pgvector), and re-ranking.
• Develop agentic workflows with tool use and orchestration; implement guardrails, evaluation harnesses, and human-in-the-loop review where appropriate.
• Apply prompt engineering, fine-tuning, and structured output techniques; measure quality with offline evals and online metrics.
• Build and maintain robust data and feature pipelines on Azure; ensure data quality, lineage, and reproducibility.
• Productionize models with sound MLOps practices: versioning, automated retraining, drift detection, and rollback strategies.
• Use AI coding assistants (Claude Code, Cursor) effectively alongside traditional manual coding — choosing the right approach for each task and reviewing AI-generated code with rigor.
• Manage source code in Git using a clean branching strategy and pull-request-based review.
• Build and maintain CI/CD pipelines (e.g., Azure DevOps, GitHub Actions) for automated testing, packaging, and deployment of AI services.
• Deploy and operate services on Azure using containers (Docker), orchestration (Kubernetes/AKS), and infrastructure-as-code (Terraform or Bicep).
• Instrument AI systems with logging, tracing, and evaluation pipelines so that quality, latency, and cost can be observed and managed over time.
• Partner with security and compliance to address data privacy, PHI/PII handling, prompt injection, and model risk in regulated contexts.
• Work directly with product, scientific, and business stakeholders to scope problems, set realistic expectations, and choose the right level of AI sophistication for the job.
• Mentor engineers on AI-assisted development practices and on the patterns and pitfalls of LLM-based systems.
• Conduct rigorous design and code reviews; advocate for evaluation, safety, and observability as first-class concerns.

Basic Qualifications

• Not provided.