Looking For AI/ML Lead, Bangalore.

NEW STREET TECHNOLOGIES • JOB DESCRIPTION

Department

Engineering • AI/ML (MiFiX.ai team)

Location

Bengaluru

Experience

58 years

Positions

1

Reports to

Head of Engineering / CTO

Role code

NST-AIL-001

Lead AI / ML Engineer

New Street Technology builds AI-powered software platforms that transform how financial institutions operate.Established in 2017, we serve 170,000+ customers across lending, trade finance, and payments recognised by theFinancial Times as the 7th fastest-growing fintech in Asia Pacific and a Deloitte Technology Fast 50 company.

About the Role

We are building an AI-native platform for regulated financial services. AI and machine learning are notfeatures bolted on — they are how the product works. We are looking for a lead engineer to own the AI/MLtrack end-to-end and grow it into a capability.

You will lead the team that builds and runs our models in production — language models, neural networks,classical ML where it earns its place. You will set the technical direction, run the experiments that matter,and ship the systems that go live for enterprise customers. This is a hands-on lead role — you write code,you read papers, and you stay close to the model.

What You'll Do

Lead all AI/ML work.

Model selection, fine-tuning, neural-network design where appropriate,evaluation, deployment — own the track and the outcomes.

Build production AI systems.

Take models from notebook to live, monitored deployment insideenterprise environments — fine-tuning, distillation/quantisation, drift detection, rollback playbooks.

Set the evaluation discipline.

Define golden datasets, regression suites, and quality gates thatdecide when a model version ships and when it does not.

Decide where AI wins.

Identify which platform tasks call for an LLM, a smaller fine-tuned model, aclassical ML model, or deterministic code — and document the reasoning.

Make it deployable.

Work with SRE on inference (CPU/GPU), batching, latency targets, and therunbook for shipping a new model version.

Grow the practice.

Mentor AI/ML engineers, set the bar for experiments and reproducibility, and hireas the team expands.

What You Bring

5+ years in ML / applied AI, with hands-on work shipping models — not just notebooks or proofs-of-concept.

Something you built must be running in production today

, and you can describe what itdoes, how it is evaluated, and how it is monitored.

Strong machine-learning and neural-network foundations — you can design, train, and debug aneural network from scratch, and you understand the trade-offs vs. classical ML or off-the-shelfLLMs.

LLM production experience — fine-tuning (SFT, LoRA/QLoRA, DPO or equivalent), promptengineering, RAG, evaluation, and guardrails on top of open or commercial models.

Strong Python — production-quality code, comfort with PyTorch and distributed training(DeepSpeed / FSDP / Accelerate).

Working knowledge of inference optimisation — quantisation (GPTQ/AWQ/bitsandbytes), servingstacks (vLLM, TGI, TensorRT-LLM), and latency/cost trade-offs.

Clear technical communicator — able to brief a CTO or compliance head on what a model can andcannot do without hand-waving.

What Would Be Great to Have

Experience shipping an on-prem or VPC-deployed model into a regulated industry (banking,healthcare, defence).

Data-curation pipeline experience — synthetic data generation, deduplication, contaminationdetection for instruction tuning.

Exposure to ML guardrails — output filtering, jailbreak resistance, RAG-grounded factuality scoring.

Hands-on with agent orchestration frameworks (LangGraph, custom DAGs) so you can reason abouthow models plug into multi-agent flows.

Tech Stack

Languages & ML frameworks:

Python 3.11+, PyTorch, Hugging Face Transformers, scikit-learn,XGBoost

Fine-tuning & training:

PEFT (LoRA / QLoRA), TRL, DeepSpeed / FSDP / Accelerate, bitsandbytes

Inference & serving:

vLLM, TGI, TensorRT-LLM, ONNX, quantisation (GPTQ / AWQ)

Orchestration & RAG:

LangGraph, LangChain, custom DAGs, pgvector / Qdrant / Weaviate

Evaluation:

RAGAS, DeepEval, custom golden-set harnesses

Backend & data:

FastAPI, PostgreSQL, Redis, Celery, Docker

Cloud:

AWS (SageMaker, EC2/GPU, S3), on-prem inference for regulated deployments

Apply:

[EMAIL]

NST-AIL-001

NewStreet • 2026 hiring

Observability:

MLflow / Weights & Biases, Prometheus, Grafana