AI Engineer (Level II) 2798

Required Skills

Python and its AI/ML ecosystem including TensorFlow, PyTorch, scikit-learn, Hugging Face Transformers, and LangChain, cloud AI/ML services such as Azure Machine Learning, Azure OpenAI Service, AWS SageMaker, or GCP Vertex AI, MLOps tools and practices including MLflow, Kubeflow, Azure ML Pipelines, and automated model retraining workflows, CNNs, RNNs, Transformers, GANs, and diffusion models

Key Responsibilities / Essential Duties

•Design and develop machine learning models, deep learning architectures, and AI solutions to address business challenges across the organization.

•Build end-to-end ML pipelines — including data ingestion, feature engineering, model training, evaluation, and deployment — using industry-standard tools and cloud platforms.

•Develop and integrate RESTful APIs and microservices to serve AI/ML models in production environments with high availability and low latency.

•Implement generative AI solutions leveraging large language models (LLMs), retrieval-augmented generation (RAG), prompt engineering, and fine-tuning techniques.

•Collaborate with data scientists, data engineers, and software development teams to translate research prototypes into scalable, production-ready applications.

•Optimize model performance through hyperparameter tuning, feature selection, A/B testing, and continuous monitoring of model drift and accuracy.

•Architect scalable AI infrastructure using cloud-native services (Azure AI, AWS SageMaker, or GCP Vertex AI) and containerization technologies (Docker, Kubernetes).

•Maintain robust MLOps practices including version control for models and data, automated retraining pipelines, CI/CD for ML workflows, and model governance.

•Ensure responsible AI practices by implementing fairness, explainability, bias detection, and compliance measures aligned with organizational policies and regulatory requirements.

•Evaluate emerging AI technologies, frameworks, and research papers to identify opportunities for innovation and competitive advantage.

•Document technical designs, model architectures, experiment results, and deployment procedures to ensure knowledge sharing and reproducibility.

•Mentor junior developers and cross-functional team members on AI/ML best practices, coding standards, and emerging technologies.

Qualifications

Education

•Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, Mathematics, or a related technical field (required).

•Master’s degree or Ph.D. in Artificial Intelligence, Machine Learning, Computer Science, or a related discipline (preferred).

Experience

•Minimum professional experience as per the Level in software development with a focus on AI/ML application development.

•Minimum of hands-on experience as per the Level building, training, and deploying machine learning models in production environments.

•Demonstrated experience with end-to-end ML pipelines and MLOps practices in a cloud-based environment.

•Experience in a regulated industry (pharmaceutical, healthcare, or life sciences) is a plus.

Certifications (Required / Preferred)

•Microsoft Certified: Azure AI Engineer Associate or AWS Certified Machine Learning — Specialty — Required (one of the two).

•Google Professional Machine Learning Engineer — Preferred.

•NVIDIA Deep Learning Institute (DLI) Certification — Preferred.

•Stanford / DeepLearning.AI Professional Certificate in Machine Learning or Deep Learning — Preferred.

Knowledge, Skills & Abilities

•Expert-level proficiency in Python and its AI/ML ecosystem including TensorFlow, PyTorch, scikit-learn, Hugging Face Transformers, and LangChain.

•Strong experience with cloud AI/ML services such as Azure Machine Learning, Azure OpenAI Service, AWS SageMaker, or GCP Vertex AI.

•Proficiency in data manipulation and analysis using Pandas, NumPy, Spark, and SQL across structured and unstructured datasets.

•Hands-on experience with MLOps tools and practices including MLflow, Kubeflow, Azure ML Pipelines, and automated model retraining workflows.

•Solid understanding of deep learning architectures including CNNs, RNNs, Transformers, GANs, and diffusion models.

•Experience with generative AI technologies including large language models (GPT, Claude, LLaMA), vector databases (Pinecone, Weaviate, FAISS), and RAG frameworks.

•Proficiency in containerization and orchestration technologies (Docker, Kubernetes) and CI/CD pipelines (Azure DevOps, GitHub Actions).

•Strong software engineering fundamentals including object-oriented design, design patterns, version control (Git), and code review best practices.

•Excellent communication skills with the ability to explain complex AI concepts to non-technical stakeholders and translate business requirements into technical solutions.

•Strong analytical and problem-solving abilities with a research-oriented mindset and commitment to staying current with rapidly evolving AI advancements.

Competencies

•Innovation & Curiosity: Actively explores emerging AI technologies and research to drive creative solutions that deliver measurable business impact.

•Technical Excellence: Maintains exceptionally high standards for code quality, model performance, and engineering best practices.

•Collaboration & Teamwork: Works effectively with cross-functional teams including data scientists, engineers, product managers, and business stakeholders.

•Adaptability: Thrives in a fast-paced environment where AI technologies and business requirements evolve rapidly.

•Responsible AI: Champions ethical AI development by proactively addressing bias, fairness, transparency, and compliance in all solutions.

•Results Orientation: Focuses on delivering production-ready AI solutions that generate tangible business value and measurable outcomes.