Senior AI ML Engineer-Team Lead

Job Title: Senior AI/ML Engineer/Team Lead Location: Gurugram,Haryana Employment Type: Full-Time Experience: 10 Years About Aaizel Tech Aaizel Tech is a pioneering tech startup at the intersection of cybersecurity, AI, geospatial solutions, and more. We drive innovation by delivering transformative technology solutions across industries. As a growing startup, we are looking for passionate and versatile professionals eager to work on cutting-edge projects in a dynamic environment.

As a Senior AI/ML Engineer at Aaizel Tech, you will lead the design, development, and deployment of advanced Machine Learning models and AI solutions. You will work on projects ranging from predictive analytics and NLP to computer vision and anomaly detection. You will also mentor a team of AI/ML professionals, collaborate with cross-functional teams, and drive innovation by integrating state-of-the-art research with scalable production systems.

1. Model Development & Optimization Design & Implementation: Architect and develop end-to-end ML solutions for applications such as predictive analytics, anomaly detection, computer vision, and NLP. Utilize advanced techniques including deep learning (CNNs, RNNs), reinforcement learning, and generative models (GANs) to address complex challenges. Optimization: Fine-tune model parameters using techniques such as hyperparameter tuning (Grid Search, Bayesian Optimization, Neural Architecture Search). Optimize models for both accuracy and inference speed to meet real-time processing

. 2. Advanced Data Engineering & Integration Data Pipeline Development: Build robust ETL pipelines using libraries like Pandas, NumPy, and PySpark to process large-scale datasets from satellite imagery, IoT sensors, and real-time streams. Integrate data from diverse sources (APIs, databases, big data platforms like Hadoop and Apache Kafka) to support real-time analytics. Data Quality & Preprocessing: Implement data cleansing, feature engineering, and transformation pipelines to ensure high-quality inputs for ML models. 3. Research & Innovation Algorithm Research: Conduct research on state-of-the-art ML techniques including Transfer Learning, Transformer models, and AutoML to enhance model performance. Innovate new algorithms for specialized tasks such as geospatial analysis, environmental modeling, or cybersecurity threat detection. Prototyping & Experimentation: Develop proof-of-concept models and prototypes to validate new approaches before production deployment. 4. Deployment, MLOps & Performance Monitoring Model Deployment: Deploy models using containerization (Docker) and orchestration tools (Kubernetes) to ensure scalable and efficient production environments. Work with cloud platforms (AWS, Azure, GCP) and model serving solutions (TensorFlow Serving, ONNX, TorchServe) for high-throughput inference. MLOps & Lifecycle Management: Implement CI/CD pipelines for ML models, ensuring seamless updates and versioning. Develop monitoring dashboards (using Prometheus, Grafana) to track model performance and trigger retraining based on real-time feedback. 5. Collaboration & Leadership Cross-Functional Teamwork: Collaborate closely with data engineers, software developers, domain experts, and product managers to integrate AI solutions into end-to-end products. Mentorship & Code Quality: Provide technical leadership and mentorship to junior AI/ML engineers, ensuring adherence to coding standards and best practices. Participate in code reviews, maintain detailed documentation, and foster a culture of continuous learning. Recommended Technology Stack Backend Framework: Python (Django/FastAPI): Ideal for API integration, leveraging Python’s rich AI/ML ecosystem. AI/ML Frameworks: PyTorch + Hugging Face Transformers + scikit-learn: For flexibility in research, multilingual NLP tasks, and classical ML pipelines. Data Engineering: Apache Kafka + Apache Spark + Apache NiFi: To handle both real-time data streaming and batch processing. Database & Storage: PostgreSQL with TimescaleDB extension: For structured and time-series data storage. DevOps & Monitoring: Docker, Kubernetes, GitLab CI/CD, Prometheus/Grafana: For containerized deployments, continuous integration, and comprehensive monitoring. Media Processing: OpenCV, FFmpeg, Tesseract OCR, Wav2Vec2: To support image, video, and speech-to-text processing where needed.