AI/ Ml Engineer

• Model Development & Fine-Tuning: Build, fine-tune, and evaluate ML and Deep Learning models for NLP, NLU, intent recognition, and conversational workflows.
• Production Deployment & Inference Optimization: Deploy models into production environments with strict latency budgets; optimize inference using techniques like quantization, pruning, and batching.
• Pipeline & MLOps Engineering: Design and maintain scalable data preprocessing, feature engineering, and model training pipelines.
• LLM & Speech Integration: Work with open-source and proprietary Large Language Models (LLMs), retrieval-augmented generation (RAG) frameworks, and voice/audio processing components (ASR/TTS).
• Monitoring & Continuous Evaluation: Implement model monitoring for drift, accuracy, and latency; automate retraining and benchmarking workflows.
• Cross-Functional Collaboration: Partner with backend and voice infrastrure.

Core Experience

2-4 years of hands-on experience in applied machine learning and deploying production models.

Programming

Strong proficiency in Python (async programming, profiling, NumPy, Pandas) and writing clean, production-grade code.

ML/DL Frameworks

Hands-on experience with PyTorch, Hugging Face Transformers, and scikit-learn.

NLP & LLMs

Practical experience with tokenization, embeddings, vector databases (e.g., Pinecone, Qdrant, Chroma), and prompt engineering/fine-tuning.

Deployment & MLOps

Experience with containerization (Docker), REST/gRPC APIs (FastAPI), model serving engines (e.g., vLLM, Triton, or ONNX Runtime), and cloud platforms (AWS / GCP).

Education

B.Tech/B.E. or M.Tech in Computer Science, Data Science, AI, or a related technical field.

Good to Have (Bonus Skills)

• Experience working with Voice AI, Speech-to-Text (STT/ASR), or Text-to-Speech (TTS) models.
• Exposure to low-latency streaming architectures (WebSockets, WebRTC, Kafka).
• Experience handling multilingual or Indic language datasets.
• Familiarity with LLM evaluation frameworks and guardrail architectures