Senior Software Engineer

Job Title: AI Engineer – Generative AI Conversational Systems

Experience Level: 3 - 5 years

Job Summary:

We are seeking a highly skilled and research-oriented Machine Learning Data Scientist with deep expertise in Generative AI, Conversational AI, Retrieval-Augmented Generation (RAG), and Agentic systems. The ideal candidate will have hands-on experience with ML/NLP tools and frameworks, and a strong track record of building and deploying scalable AI applications across cloud platforms (AWS, Azure, GCP) and on-premise environments. You will be responsible for developing next-generation AI solutions that enable intelligent conversations, decision support, automation, and seamless human-AI collaboration.

Key Responsibilities

Conversational AI LLM Engineering:

• Design and develop conversational AI models (chat and voice) using LLMs such as GPT, LLaMA, Claude etc.
• Implement RAG pipelines to enhance model performance using contextual and external knowledge sources.
• Build Agentic AI workflows using frameworks like LangGraph to manage complex, multi-step interactions requiring reasoning.
• Perform model fine-tuning, prompt engineering, and embedding optimization.

NLP ML Engineering:

• Perform data cleaning, preprocessing, and analysis on structured and unstructured text using libraries like NLTK, spaCy, and pandas.
• Utilize Hugging Face Transformers for model training, inference, and deployment in production.
• Develop reusable components and prompt strategies for multi-turn dialogues and dynamic intent detection.
• Build and evaluate models for tasks such as text classification, summarization, information extraction, and semantic search.
• Conduct exploratory data analysis and feature engineering across varied datasets.

Deployment Observability:

• Deploy models and services across AWS (SageMaker, Lambda, Bedrock) and on-premises using Kubernetes and Docker.
• Implement monitoring and observability pipelines using tools such as Prometheus, Grafana, OpenTelemetry, and ML observability stacks.
• Ensure production-grade systems are equipped with real-time metrics, traceability, and failover capabilities.

Required Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Data Science, AI/ML, or a related field.
• Minimum 3+ years of experience in machine learning and NLP, with at least 1–2 years working on LLMs or RAG systems.
• Proficiency in Python and strong experience with NLP libraries such as NLTK, Hugging Face, LangChain, and LangGraph.
• Experience with ML frameworks like PyTorch, TensorFlow, Keras, XGBoost, or Scikit-learn.
• Hands-on expertise in LLM fine-tuning, prompt engineering, and retrieval pipeline optimization.
• Solid understanding of vector search technologies (e.g., FAISS, Pinecone) for semantic retrieval and context enhancement.
• Proven experience in deploying ML solutions in production on cloud and on-prem infrastructure.
• Familiarity with observability, monitoring, and reliability engineering practices for conversational AI systems.
• Experience with RESTful APIs, WebSockets, and real-time event-driven system design.