Artificial Intelligence Engineer

Key Responsibilities

• Design, build, and optimize RAG pipelines, including prompting, text chunking, retrieval, reranking, and evaluation using vector databases such as Chroma DB or Qdrant, and Transformer-based LLMs like Llama, Mistral, or BERT family models.
• Productionize ASR systems (e.g., Whisper-large-v3) for call centre and voice-based use cases, ensuring improvements in both accuracy and latency.
• Develop OCR and document digitization workflows using tools such as OpenCV and Tesseract, along with CNN/LSTM-based post-processing for unstructured PDFs and images.
• Build and deploy APIs using Fast API or Flask, integrating with existing services and data sources such as MongoDB.
• Orchestrate data and model workflows using Airflow, automating ETL processes, evaluation pipelines, and periodic retraining.
• Implement CI/CD pipelines for model and API releases, ensuring strong testing, logging, and observability practices.
• Manage both offline and online evaluation for metrics such as latency, accuracy, F1 score, ASR WER, and retrieval precision/recall, and provide detailed analytical reports and recommendations.
• Collaborate closely with product and operations teams to translate business challenges into measurable ML objectives and service-level agreements (SLAs).

Must-Have Skills

• Proficiency in Python (production-grade), PyTorch, and Hugging Face Transformers.
• Strong understanding of RAG fundamentals, including text chunking, embedding selection, retrieval (dense and sparse), reranking, and evaluation frameworks.
• Experience with vector search and data stores such as Chroma DB or similar technologies, along with solid data modelling and indexing expertise.
• Practical knowledge of API development using Fast API or Flask, including RESTful best practices, authentication, rate limiting, and pagination.
• Experience in MLOps using Docker, CI/CD, Linux, Git, and tools for logging and monitoring model services.
• Exposure to OCR and ASR systems, particularly OpenCV, Tesseract, and Whisper (or equivalent frameworks).
• Strong grasp of classical NLP and ML techniques, including tokenization, LSTMs/CNNs, XGBoost, and metric-driven experimentation.

Good-to-Have Skills

• Experience fine-tuning large language models or encoders for classification, summarization, and domain adaptation.
• Understanding of prompt engineering, tool integration, and evaluation for LLM-based applications.
• Familiarity with scaling retrieval systems for low-latency, high-availability production environments.
• Experience with document question answering, email or call centre analytics, or enterprise knowledge management.
• Knowledge of agentic AI frameworks such as Lang Chain, Lang Graph, or Crew AI.

Qualifications

• 3-6 years of hands-on experience in AI, ML, or NLP engineering with proven production ownership.
• Bachelor's degree in computer science, or a related field, or equivalent practical experience.
• Strong portfolio or GitHub profile demonstrating shipped APIs, model implementations, and well-documented repositories with test coverage.