SR. AI Developer

3 days ago

Bengaluru, Karnataka, India TE Connectivity Full time ₹ 12,00,000 - ₹ 24,00,000 per year

*Job Overview*

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

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