Job Title: Machine Learning Engineer
Job Summary:
We are seeking an Expert Machine Learning Engineer to lead the design, development, and deployment of advanced AI-driven solutions. The ideal candidate should have extensive experience in deep learning, reinforcement learning, scalable ML architectures, cloud-based AI solutions, and MLOps best practices. This role involves leading AI research, mentoring ML teams, optimizing large-scale models, and integrating AI innovations into enterprise applications.
Key Responsibilities:
- Architect, develop, and deploy large-scale machine learning models for real-world AI applications
- Lead cutting-edge AI research and innovation, integrating state-of-the-art deep learning models (CNNs, RNNs, Transformers, GANs, and LLMs)
- Develop and optimize distributed ML models using HPC, parallel processing, and federated learning techniques
- Implement MLOps frameworks, ensuring model retraining, version control, CI/CD automation, and real-time monitoring
- Optimize AI models for low latency and high throughput for production environments
- Work with massive datasets, leveraging big data pipelines and distributed computing frameworks (Spark ML, Dask, Ray)
- Deploy ML models at scale on cloud and edge computing platforms (AWS SageMaker, Google Vertex AI, Azure ML, Snowflake ML)
- Lead AI governance, fairness, explainability, and security initiatives to build trustworthy AI solutions
- Collaborate with executive stakeholders, research scientists, and engineering teams to align AI strategies with business goals
- Drive AI adoption and best practices across the organization, mentoring junior and senior ML engineers
- Stay at the forefront of AI advancements, publishing research, attending conferences, and integrating cutting-edge innovations
Skills and Knowledge Required:
- Expert proficiency in Python (R, Scala, or Julia is a plus)
- Mastery of ML/DL frameworks (TensorFlow, PyTorch, JAX, Scikit-learn, Keras)
- Deep expertise in AI architectures, including transformers (BERT, GPT, T5), GANs, reinforcement learning (DQN, PPO), and meta-learning
- Proficiency in big data processing & parallel computing (Spark ML, Dask, Ray, Apache Flink)
- Experience with cloud AI platforms (AWS SageMaker, Google Cloud AI, Azure ML, Snowflake)
- Strong expertise in MLOps tools (MLflow, Kubeflow, Airflow, Vertex AI, SageMaker Pipelines)
- Hands-on experience with automated hyperparameter tuning (Optuna, Bayesian Optimization, HyperOpt)
- Ability to deploy ML models as scalable APIs using Docker, Kubernetes, TensorRT, ONNX
- Experience in AI model security, adversarial robustness, and federated learning
- Familiarity with quantum machine learning (QML) and edge AI models (optional but a plus)
- Deep understanding of AI ethics, bias detection, explainability (SHAP, LIME), and compliance frameworks
Educational Qualifications:
- Master’s or PhD in Computer Science, AI, Data Science, Mathematics, or related fields
- Certifications in Deep Learning, Cloud AI, or MLOps (AWS/GCP/Azure, Coursera, Udacity, MIT AI) preferred
Experience:
- 10+ years of experience in AI/ML research, enterprise AI deployment, and production-grade ML systems
- Proven track record in leading AI-driven projects, publishing research, and mentoring AI teams
- Experience in handling multi-terabyte datasets and optimizing AI models for large-scale applications
Key Focus Areas:
- Next-Generation AI Research & Development
- Enterprise AI & Scalable Machine Learning Architectures
- MLOps & Automated AI Model Deployment
- Deep Learning Optimization & Hyperparameter Tuning
- AI Governance, Fairness, and Ethical AI Practices
Tools and Technologies:
- Programming Languages: Python (R, Scala, Julia optional)
- ML Frameworks: TensorFlow, PyTorch, JAX, Scikit-learn, Keras
- Big Data & Distributed ML: Apache Spark, Dask, Ray, Flink
- Cloud Platforms: AWS SageMaker, Google Vertex AI, Azure ML, Snowflake ML
- Deployment & MLOps: Kubernetes, MLflow, Airflow, FastAPI, Flask, ONNX, TensorRT
- Data Engineering & Storage: SQL, NoSQL, Delta Lake, Snowflake, BigQuery
- Version Control & CI/CD: Git, Terraform, Jenkins, Kubeflow
Other Requirements:
- Proven leadership in AI strategy, ML research, and deployment at scale
- Ability to bridge research and real-world AI implementation
- Exceptional problem-solving skills for tackling complex AI challenges
- Passion for AI ethics, fairness, and responsible AI