Job Description

L
ead development of AI solutions using supervised, unsupervised, and reinforcement learning techniques. C
ollaborate with data scientists, software engineers, and product teams to translate business requirements into intelligent systems. O
ptimize model performance, latency, and resource utilization for production-grade deployments. E
valuate and integrate third-party AI services, frameworks, and APIs where appropriate. S
trong proficiency in Python and ML libraries (TensorFlow, PyTorch, Scikit-learn, XGBoost). E
xperience with NLP, Computer Vision, Time Series Forecasting, and Recommendation Systems. D
eep understanding of data preprocessing, feature engineering, and model evaluation techniques. H
ands-on experience with MLOps tools (MLflow, Kubeflow, SageMaker, Azure ML). E
xposure to LLMs and generative AI frameworks (Hugging Face Transformers, LangChain, OpenAI APIs). F
amiliarity with vector databases (FAISS, Pinecone, Weaviate) and embedding techniques. E
xperience with cloud platforms (Azure, AWS, GCP) and container orchestration (Docker, Kubernetes). K
nowledge of big data ecosystems (Spark, Hadoop, Databricks) and real-time data streams (Kafka, Flink). P
roficiency in SQL and NoSQL databases (PostgreSQL, MongoDB, Redis). U
nderstanding of CI/CD pipelines and DevOps practices for AI/ML workflows. E
xperience with model interpretability and explainability tools (SHAP, LIME). K
nowledge of Responsible AI principles and bias mitigation strategies. F
amiliarity with edge AI deployment (ONNX, TensorRT, Coral, NVIDIA Jetson). E
xposure to graph-based ML and knowledge graphs. P
roven ability to lead cross-functional teams and drive delivery in agile environments. S
trong problem-solving mindset with a bias toward experimentation and iteration. E
xcellent communication and stakeholder management skills. A
bility to evaluate alternative solutions and articulate technical decisions clearly. P
assion for staying current with AI trends, research, and emerging technologies.

H