Advanced Software Engr
Summary
Build and deploy AI/ML models in production, leading teams to turn business needs into intelligent systems using Python, TensorFlow, and cloud platforms like Azure.
- 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.