Machine Learning Engineer

Overview We are seeking a highly experienced Machine Learning Engineer to design, develop, and deploy scalable AI/ML solutions in enterprise environments. The role focuses on building intelligent systems leveraging Generative AI, predictive analytics, and large-scale telemetry data to drive operational insights and automation.

Key Responsibilities AI/ML Model Development

• Design and implement machine learning and deep learning models for:
• Anomaly detection
• Predictive analytics
• Classification and clustering
• Develop and optimize model training pipelines for large-scale datasets
• Perform feature engineering, model evaluation, and hyperparameter tuning

Generative AI & NLP

• Build and deploy

GenAI solutions using:

• OpenAI APIs
• LangChain frameworks
• Develop capabilities such as:
• Text summarization
• Conversational AI
• Context-aware question answering (RAG architectures)
• Implement NLP pipelines for unstructured data processing

Data Engineering & Pipelines

• Design scalable pipelines for processing

high-volume telemetry data (metrics, logs, events, traces)

• Work with streaming and batch processing systems (Kafka, Spark, etc.)
• Ensure

data quality, transformation, and enrichment Model Deployment & MLOps

• Deploy ML models using

containerization (Docker) and orchestration (Kubernetes)

• Implement

CI/CD pipelines for ML lifecycle management

• Monitor model performance and ensure

continuous improvement Integration & APIs

• Develop and expose ML services via

REST APIs (FastAPI, Flask)

• Integrate AI services with enterprise systems and applications

Qualifications & Skills ✅ Experience

• 5+ years in Machine Learning / Data Science roles in enterprise environments

Technical Expertise

• Programming: Python (mandatory)
• ML/DL: TensorFlow, PyTorch, Scikit-learn
• GenAI: OpenAI, LangChain, RAG pipelines
• Data Engineering: Spark, Kafka, ETL frameworks
• APIs: FastAPI, Flask
• Databases: SQL, PostgreSQL, NoSQL