Advanced Software Engr

Honeywell · Industrial · Bengaluru, Karnataka, India

Advanced Software Engineer at Honeywell in Bengaluru, India, focusing on Data Engineering and ML Ops for production-grade AI solutions. The role involves leading the development of AI systems using various ML techniques, optimizing deployed models, and integrating third-party AI services. Requires strong Python and ML library skills, experience with MLOps tools, cloud platforms, and big data ecosystems. Familiarity with LLMs, generative AI, vector databases, and edge AI deployment is also expected.

What you'd actually do

Lead development of AI solutions using supervised, unsupervised, and reinforcement learning techniques.
Optimize model performance, latency, and resource utilization for production-grade deployments.
Evaluate and integrate third-party AI services, frameworks, and APIs where appropriate.
Collaborate with data scientists, software engineers, and product teams to translate business requirements into intelligent systems.
6+ years of experience in, Data Engineering & ML OPS, with strong exposure to real-world, production-grade models.

Skills

Required

Python
ML libraries (TensorFlow, PyTorch, Scikit-learn, XGBoost)
MLOps tools (MLflow, Kubeflow, SageMaker, Azure ML)
cloud platforms (Azure, AWS, GCP)
container orchestration (Docker, Kubernetes)
big data ecosystems (Spark, Hadoop, Databricks)
real-time data streams (Kafka, Flink)
SQL
NoSQL databases (PostgreSQL, MongoDB, Redis)
CI/CD pipelines
DevOps practices for AI/ML workflows
model interpretability and explainability tools (SHAP, LIME)
Responsible AI principles
bias mitigation strategies
edge AI deployment (ONNX, TensorRT, Coral, NVIDIA Jetson)
graph-based ML
knowledge graphs
agile environments

Nice to have

NLP
Computer Vision
Time Series Forecasting
Recommendation Systems
LLMs
generative AI frameworks (Hugging Face Transformers, LangChain, OpenAI APIs)
vector databases (FAISS, Pinecone, Weaviate)
embedding techniques

What the JD emphasized

production-grade models
production-grade deployments
production-grade

Other signals

production-grade models
ML OPS
optimize model performance, latency, and resource utilization

Read full job description

6+ years of experience in, Data Engineering & ML OPS, with strong exposure to real-world, production-grade models.
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.

6+ years of experience in, Data Engineering & ML OPS, with strong exposure to real-world, production-grade models.
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.