Senior AI/ML Engineer

Stigasoft -
Mumbai, Maharashtra

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Job details

Full-time
1 hour ago

Qualifications

TensorFlow
CI/CD
Azure
Rust (programming language)
Go
Node.js
Kubernetes
PyTorch
Spark
AWS
Docker
Distributed systems
Continuous integration
Natural language processing
APIs
AI
gRPC
Python

Full job description

Openings: 1
Exp: 7+years
Mumbai
Full-Time

We seek a seasoned AI/ML Engineer specializing in Natural Language Processing (NLP) and Transformer architectures to design, optimize, and deploy cutting-edge language models (e.g., BERT, GPT, T5) for real-world applications. You’ll own the end-to-end ML lifecycle - from research to rapid inferencing - leveraging distributed computing, container orchestration (Kubernetes), and microservices to serve models at scale.

Key Responsibilities

Model Development & Optimization

Research, fine-tune, and deploy Transformer-based models (e.g., LLMs, encoder-decoder architectures) for tasks like text generation, summarization, and entity recognition.
Optimize models for low-latency inferencing (e.g., quantization, distillation, ONNX runtime).
Architect and deploy low-latency inference pipelines, optimizing for throughput (10K+ RPS) using Triton, vLLM, or TensorRT.
Implement distributed training (PyTorch FSDP, Horovod) across GPU clusters (AWS/GCP/Azure).
Implement distributed model serving (multi-GPU, multi-node) with Kubernetes/Knative and autoscaling.
Pioneer hybrid cloud strategies (e.g., on-prem GPU clusters + cloud burst) for cost-efficient training/inference.

ML Pipeline & Infrastructure

Architect scalable ML pipelines (feature store, model registry, CI/CD) using MLflow, Kubeflow, or TFX.
Containerize models (Docker) and orchestrate via Kubernetes for high-availability deployments.
Design microservices (FastAPI, gRPC) to expose models as APIs with <100ms latency.

Performance & Observability

Monitor model performance (drift, accuracy) using Prometheus/Grafana.
Implement A/B testing and shadow deployments for zero-downtime updates.

Collaboration & Leadership

Mentor junior engineers and collaborate with Data Scientists, DevOps, and Product Teams.
Translate business requirements into SOTA NLP solutions with measurable ROI.

Required Skills & Qualifications

Technical Expertise:

7+ years in NLP, with 3+ years focused on Transformer models (e.g., BERT, RoBERTa, GPT variants).

Mastery of PyTorch/TensorFlow, Hugging Face transformers, and distributed training (DDP, DeepSpeed), distributed systems (Ray, Horovod, Spark).

Production experience with Kubernetes, Docker, and cloud ML services (SageMaker, Vertex AI).

Strong Python skills (asyncio, multiprocessing) and familiarity with Go/Rust for high-performance services.

Deployment & Ops:

Proven track record of deploying low-latency model APIs (FastAPI, Triton Inference Server).

Knowledge of ML observability tools (Weights & Biases, Evidently).

Can't find your role? Email us your resume at [email protected]

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Key Responsibilities

Model Development & Optimization

ML Pipeline & Infrastructure

Performance & Observability

Collaboration & Leadership

Technical Expertise:

Deployment & Ops:

Can't find your role? Email us your resume at [email protected]

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