Senior AI Engineer

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Requirements:

• Expert-level Python skills, with experience in modular, testable, production-quality code.
• 5+ years of software engineering experience (with preference in industrial enterprise application related areas such as manufacturing, aerospace & defense)
• 1.5+ years of experience in AI/ML engineering roles, ideally in enterprise or manufacturing-focused products.
• Hands-on experience with the Hugging Face tools, including Transformers, Trainer, Datasets, and model hub integration.
• Demonstrated experience developing AI-driven applications that leverage RAG, tool calling, and/or agent orchestration via frameworks like LangChain, LangFlow, or LlamaIndex and infrastructure components like Vector Databases and Document Parsing tools.
• Proficiency in PyTorch, with working knowledge of TensorFlow, scikit-learn, Flask.
• Proven experience with LLM training, fine-tuning, and prompt engineering.
• Strong grasp of NLP fundamentals, transformer-based architectures, embeddings, and vector search.
• Experience deploying models via containerized services using tools like Docker and FastAPI.
• Familiarity with cloud ML platforms (e.g., AWS SageMaker, Azure ML, GCP Vertex AI).
• Knowledge of model monitoring, explainability (XAI), and security practices for production AI.
• Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or a related field.

Responsibilities:

• Design, develop, and deploy scalable AI models for Solumina AI offerings including knowledge retrieval, anomaly detection, document parsing, and intelligent reporting.
• Fine-tune and deploy LLMs using the Hugging Face ecosystem (Transformers, Datasets, Evaluate, Accelerate).
• Build and maintain AI pipelines leveraging Python, PyTorch, and FastAPI to enable model deployment and inference at scale.
• Collaborate with cross-functional teams to define and implement domain-specific reasoning models for manufacturing and aerospace workflows.
• Develop future-ready agentic AI workflows to support multi-step task automation and planning.
• Own the end-to-end ML lifecycle—from data ingestion and preprocessing to model training, evaluation, deployment, and monitoring.
• Design robust APIs and microservices to integrate AI into the Solumina platform and external interfaces.
• Apply best practices in responsible AI—ensuring bias mitigation, security, explainability, and traceability.
• Stay current with state-of-the-art AI research, with a focus on foundation models, reasoning agents, and GenAI applications in industrial use cases.
• Mentor junior engineers and contribute to team-wide best practices in AI architecture and MLOps.