Job Description

Solves complex problems and help stakeholders make data- driven decisions by leveraging quantitative methods, such as machine learning. It often involves synthesizing large volume of information and extracting signals from data in a programmatic way.

Roles & Responsibilities

Deep Learning

Architect and train

CNN/ViT models

for classification, detection, segmentation, and OCR. Build and optimize

RNN/LSTM/GRU models

for sequence learning, speech, or timeseries forecasting. Research and implement

transformer-based architectures

bridging vision and language tasks. Create scalable pipelines for data ingestion, annotation, augmentation, and synthetic data generation.

Agentic AI & Multi-Agent Frameworks

Design and implement multi-agent workflows using LangChain, LangGraph, CrewAI, or similar frameworks. Develop role hierarchies, state graphs, and integrations that enable

autonomous

vision + language workflows

. Optimize agent systems for

latency, cost, and reliability

.

LLM Fine-Tuning & Retrieval-Augmented Generation (RAG)

Fine-tune open-weight LLMs using LoRA/QLoRA, PEFT, or RLHF methods. Develop

RAG pipelines

integrating vector databases (FAISS, Weaviate, pgvector). Combine LLM reasoning with CNN/RNN perception modules in multimodal systems.

MLOps & Deployment at Scale

Develop reproducible training workflows with PyTorch/TensorFlow and experiment tracking (W&B, MLflow). Deploy models with TorchServe, Triton, or KServe on cloud AI stacks (AWS Sagemaker, GCP Vertex, Kubernetes). Optimize inference with ONNX/TensorRT, quantization, and pruning for

cloud and

edge devices

. Build robust APIs/micro-services (FastAPI, gRPC) and ensure CI/CD, monitoring, and automated retraining. Desired Candidate
B.S./M.S. in Computer Science, Electrical Engineering, Applied Math, or related discipline.

5+ years

building deep learning systems with

CNNs and RNNs in production

. Strong Python skills and Git workflows. Proven delivery of

computer vision pipelines (OCR, classification, detection)

. Hands-on experience with

LLM fine-tuning and multimodal AI

. Experience in

containerization (Docker)

and deployment on cloud AI platforms. Knowledge of

distributed training, GPU acceleration, and inference

optimization

.

Preferred Qualifications

Research experience in

transformer architectures (ViTs, hybrid CNN-RNNTransformer

models)

. Prior work in

sequence modeling for speech or time-series data

. Contributions to

open-source deep learning frameworks or vision/sequence

datasets

. Experience with

edge AI deployment

and hardware optimization

Competencies

Values: Integrity, Accountability, Inclusion, Innovation, Teamwork

Job Locations:

Bangalore, Karn?taka 560076

India

Travel Required:

10% to 25%

Requisition ID:

142959

Workplace Type:

External Careers Page:

Information Technology