Generative AI Engineer

About the Role We are looking for an

AI Engineer to work on an enterprise AI platform that is transforming how public-sector organizations evaluate tenders and procurement documents. Our platform uses

Generative AI, Agentic AI, document intelligence and open-source foundation models to automate complex document-heavy workflows involved in tender evaluation. The product is already deployed in large

Public Sector Undertakings (PSUs) and runs on

NVIDIA GPU infrastructure using open-source AI models. This role sits at the intersection of

AI research and production engineering . You will experiment with new models and techniques, but the goal is not research for its own sake — it is to

improve accuracy, reduce latency and cost, and make the product increasingly autonomous. What You Will Work On

• Experiment with and evaluate the latest

open-source LLMs and VLMs for document understanding, extraction, reasoning and classification.

• Benchmark models for

accuracy, latency, throughput and GPU utilization and make recommendations for production adoption.

• Optimize AI inference pipelines to improve

response time, throughput and infrastructure efficiency .

• Develop and deploy

Agentic AI workflows that can autonomously execute multi-step document processing and tender evaluation tasks.

• Design agents capable of planning, reasoning, tool use, information retrieval and validation across complex procurement workflows.
• Build production-grade pipelines for processing

large, complex and highly unstructured documents .

• Work on techniques such as

RAG, structured extraction, reranking, model routing, prompt engineering and evaluation .

• Fine-tune or adapt models where appropriate to improve performance on domain-specific procurement tasks.
• Work closely with product and domain teams to translate business requirements into AI capabilities.
• Profile and optimize GPU workloads across

NVIDIA GPU infrastructure .

• Develop robust evaluation frameworks to continuously measure

LLM accuracy, hallucination, latency and reliability .

• Stay current with developments in open-source models, inference frameworks and agentic AI, and rapidly prototype promising approaches.

What We’re Looking For Must Have

• 3–5 years of hands-on experience building and deploying AI/ML systems.
• Strong

Python programming and software engineering fundamentals.

• Strong understanding of

Generative AI / LLMs and modern AI application architectures.

• Hands-on experience deploying models in production environments.
• Experience with

open-source LLMs such as Llama, Qwen, Mistral, DeepSeek or similar models.

• Experience with

GPU-based inference and optimization .

• Strong understanding of model performance metrics including

latency, throughput, concurrency and GPU utilization .

• Experience with

Docker and Linux ; Kubernetes is a strong plus.

• Ability to read research papers, understand new techniques and turn them into working production systems.
• Strong problem-solving mindset and willingness to experiment rapidly.

Good to Have

• Experience with

NVIDIA GPUs, CUDA, TensorRT, Triton Inference Server, vLLM or NVIDIA NIM .

• Experience with

Agentic AI / AI agents , tool calling, orchestration and multi-agent workflows.

• Experience with

RAG, vector databases, embeddings and reranking .

• Experience with

LLM/VLM evaluation and benchmarking .

• Experience with document AI, OCR, document understanding or multimodal models.
• Experience with

PyTorch, Hugging Face Transformers and PEFT/LoRA .

• Familiarity with cloud or on-prem GPU deployments.
• Experience working with large-scale enterprise AI deployments.

What Success Looks Like In this role, you will be expected to move beyond simply integrating LLM APIs. You will help us answer questions such as:

• Can we make the model 20% more accurate?
• Can we reduce inference latency by 30%?
• Can we run the workload on fewer GPUs?
• Can an AI agent autonomously complete a document evaluation workflow that currently requires human intervention?
• Can we reliably deploy a new open-source model into production without compromising quality? You will have significant ownership in taking these ideas from

experiment → benchmark → engineering → production .

Why Join Us? You will work on an AI product that is already solving

real enterprise problems in production , rather than building prototypes that never leave the lab. The role offers an opportunity to work across:

Open-source AI models → AI research → GPU optimization → Agentic AI → Production engineering → Enterprise deployment If you enjoy going deep into models, experimenting with new AI techniques, writing production-grade code and seeing your work directly improve a live AI product, this role is for you.