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 (Tensor Flow, Py Torch, 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, Sage Maker, Azure ML).

Exposure to LLMs and generative AI frameworks (Hugging Face Transformers, Lang Chain, 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).