Honeywell helps organizations solve the world's most complex challenges in automation, the future of aviation and energy transition. As a trusted partner, we provide actionable solutions and innovation through our Aerospace Technologies, Building Automation, Energy and Sustainability Solutions, and Industrial Automation business segments – powered by our Honeywell Forge software – that help make the world smarter, safer and more sustainable.

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As a Sr Advanced Software Engr here at Honeywell, you will lead a team of software engineers, ensuring successful project execution and high-quality solutions. Collaborate with cross-functional teams to meet software engineering requirements. You will report directly to our Manager and you’ll work out of our Bengaluru, Karnataka location on a Hybrid work schedule.

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• Lead development of AI solutions using supervised, unsupervised, and reinforcement learning techniques.
• Collaborate with data scientists, software engineers, and product teams to translate business requirements into intelligent systems.
• Optimize model performance, latency, and resource utilization for production-grade deployments.
• Evaluate and integrate third-party AI services, frameworks, and APIs where appropriate.
• Strong proficiency in Python and ML libraries (TensorFlow, PyTorch, Scikit-learn, XGBoost).
• Experience with NLP, Computer Vision, Time Series Forecasting, and Recommendation Systems.
• Deep understanding of data preprocessing, feature engineering, and model evaluation techniques.
• Hands-on experience with MLOps tools (MLflow, Kubeflow, SageMaker, Azure ML).
• Exposure to LLMs and generative AI frameworks (Hugging Face Transformers, LangChain, OpenAI APIs).
• Familiarity with vector databases (FAISS, Pinecone, Weaviate) and embedding techniques.
• Experience with cloud platforms (Azure, AWS, GCP) and container orchestration (Docker, Kubernetes).
• Knowledge of big data ecosystems (Spark, Hadoop, Databricks) and real-time data streams (Kafka, Flink).
• Proficiency in SQL and NoSQL databases (PostgreSQL, MongoDB, Redis).
• Understanding of CI/CD pipelines and DevOps practices for AI/ML workflows.
• Experience with model interpretability and explainability tools (SHAP, LIME).
• Knowledge of Responsible AI principles and bias mitigation strategies.
• Familiarity with edge AI deployment (ONNX, TensorRT, Coral, NVIDIA Jetson).
• Exposure to graph-based ML and knowledge graphs.
• Proven ability to lead cross-functional teams and drive delivery in agile environments.
• Strong problem-solving mindset with a bias toward experimentation and iteration.
• Excellent communication and stakeholder management skills.
• Ability to evaluate alternative solutions and articulate technical decisions clearly.
• Passion for staying current with AI trends, research, and emerging technologies.