We are looking for a Senior Software Engineer (AI/ML) who can design, build, and scale intelligent systems — from machine learning pipelines to advanced AI agents capable of reasoning, planning, and automation.
You will work on building production-grade AI models, integrating LLMs and tools, and designing agent architectures that interact with APIs, databases, and workflows. The role blends applied ML expertise with strong backend engineering and product-focused problem-solving.
Responsibilities:
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Design and build autonomous or semi-autonomous AI agents that can plan, reason, and interact with tools, APIs, or external systems
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Implement agentic frameworks (e.g., LangChain, LlamaIndex, CrewAI, or custom orchestration systems)
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leverage existing industry capabilities to deliver virtual assistant capabilities on top of xquic content including voice interactions
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Optimize reasoning and retrieval pipelines using embeddings, vector databases, and prompt engineering
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Develop, train, and fine-tune ML models using frameworks like PyTorch, TensorFlow, or scikit-learn
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Work on data preprocessing, feature engineering, and model evaluation for NLP, computer vision, or predictive tasks
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Build ML pipelines for training, deployment, and monitoring in production environments
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Collaborate with engineering teams to integrate AI components into backend systems and APIs
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Ensure scalable, maintainable codebases with CI/CD, observability, and cloud-native design (AWS/GCP/Azure)
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Contribute to technical architecture and design reviews for AI-driven features and platforms
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Stay current with the latest in LLMs, agent frameworks, and model architectures
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Prototype and evaluate new approaches for reasoning, tool use, and adaptive behavior in agents
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Share learnings and mentor peers in ML and AI development best practices
Requirements
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Strong programming skills in Python (mandatory); proficiency with PyTorch,
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TensorFlow, or transformers-based models
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Experience in building or integrating AI agents (LangChain, LlamaIndex, CrewAI, custom frameworks)
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Strong grasp of ML model lifecycle — data processing, model training, evaluation, deployment, and monitoring
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Experience with cloud platforms (AWS/GCP/Azure) and containerization (Docker, Kubernetes)
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Familiarity with API integrations, microservices, and asynchronous systems
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Strong understanding of vector databases (e.g., Pinecone, Weaviate, FAISS, Chroma) and retrieval architectures
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Solid software engineering fundamentals — testing, version control, and system design
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Experience with LLM fine-tuning, prompt optimization, or RAG (Retrieval-Augmented Generation) systems
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Familiarity with multi-agent systems and coordination mechanisms
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Knowledge of MLOps tools (MLflow, Kubeflow, Sagemaker, Vertex AI)
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Experience in AI system observability, evaluation metrics, and continuous learning loops
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Exposure to reinforcement learning or self-improving agent architectures
Benefits
Provident Fund, Medical Inpatient Facility, Medical Outpatient Facility, Paid Overtime, In-house Subsidized Lunch & Dinner, Gym Facility, Entertaining Activities, Interest Free Loan Facility, Advance Salaries and Sports Allowance.