Senior Software Engineer (AI/ML)

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.

- Design and build autonomous or semi-autonomous AI agents that can plan, reason, and interact with tools, APIs, or external systems.

- Implement agentic frameworks (e.g., LangChain, LlamaIndex, CrewAI, or custom orchestration systems).

- leverage existing industry capabilities to deliver virtual assistant capabilities on top of xquic content including voice interactions.

- Optimize reasoning and retrieval pipelines using embeddings, vector databases, and prompt engineering.

- Develop, train, and fine-tune ML models using frameworks like PyTorch, TensorFlow, or scikit-learn.

- Work on data preprocessing, feature engineering, and model evaluation for NLP, computer vision, or predictive tasks.

- Build ML pipelines for training, deployment, and monitoring in production environments.

- Collaborate with engineering teams to integrate AI components into backend systems and APIs.

- Ensure scalable, maintainable codebases with CI/CD, observability, and cloud-native design (AWS/GCP/Azure).

- Contribute to technical architecture and design reviews for AI-driven features and platforms.

- Stay current with the latest in LLMs, agent frameworks, and model architectures.

- Prototype and evaluate new approaches for reasoning, tool use, and adaptive behavior in agents.

- Share learnings and mentor peers in ML and AI development best practices.

- Bachelors Degree in Computer Science or related fields

- 3+ years of experience

- Strong programming skills in Python (mandatory); proficiency with PyTorch,

- TensorFlow, or transformers-based models.

- Experience in building or integrating AI agents (LangChain, LlamaIndex, CrewAI, custom frameworks).

- Strong grasp of ML model lifecycle — data processing, model training, evaluation, deployment, and monitoring.

- Experience with cloud platforms (AWS/GCP/Azure) and containerization (Docker, Kubernetes).

- Familiarity with API integrations, microservices, and asynchronous systems.

- Strong understanding of vector databases (e.g., Pinecone, Weaviate, FAISS, Chroma) and retrieval architectures.

- Solid software engineering fundamentals — testing, version control, and system design.

- Experience with LLM fine-tuning, prompt optimization, or RAG (Retrieval-Augmented Generation) systems.

- Familiarity with multi-agent systems and coordination mechanisms.

- Knowledge of MLOps tools (MLflow, Kubeflow, Sagemaker, Vertex AI).

- Experience in AI system observability, evaluation metrics, and continuous learning loops.

- Exposure to reinforcement learning or self-improving agent architectures.