Python AI Engineer | ML & Cloud Deployment

Job Summary Synechron is seeking a skilled Python AI Engineer to design, develop, and deploy AI-driven solutions across enterprise platforms. This role emphasizes hands-on AI development, collaboration with data scientists and engineers, and delivering scalable, secure, production-ready models and workflows. The ideal candidate will contribute to advancing AI capabilities, mentor teammates, and ensure alignment with governance, risk, and compliance requirements while driving business value.

Software Requirements Required Skills (Essential)

• Proficiency in Python 3.x for AI/ML development, data processing, and orchestration
• Hands-on experience with AI frameworks such as TensorFlow and PyTorch
• Experience developing and deploying machine learning models, including NLP, CV, or tabular ML, in production
• Strong knowledge of cloud platforms (AWS, Azure, or GCP) for model training, deployment, and monitoring
• Experience with data processing libraries (Pandas, NumPy) and data manipulation
• Proficiency with version control systems (Git) and collaboration platforms (GitHub, GitLab, Bitbucket)
• Familiarity with SDLC/Agile methodologies and cross-functional teamwork
• Experience designing and implementing APIs to expose AI capabilities (REST/GraphQL)
• Strong SQL knowledge and data management skills for training/validation data
• Basic understanding of data governance, privacy, and security considerations in AI

Preferred Skills

• Experience with MLOps tooling (model versioning, monitoring, retraining pipelines)
• Containerization (Docker) and orchestration (Kubernetes) for AI services
• Experience with model monitoring and bias/safety considerations
• Familiarity with data visualization or BI integrations to present AI results
• Knowledge of data pipelines, data quality, and governance in AI projects

Overall Responsibilities

• Design, develop, and deploy AI models and AI-driven solutions across business domains
• Collaborate with data scientists, software engineers, product managers, and business stakeholders to translate requirements into scalable AI solutions
• Build and optimize end-to-end ML workflows, including data ingestion, feature engineering, model training, evaluation, and deployment
• Ensure governance, risk, and compliance considerations are embedded in AI initiatives
• Mentor and coach junior AI engineers; promote best practices in AI development and MLOps
• Stay current with AI/ML trends and incorporate cutting-edge techniques into projects
• Develop and maintain documentation for model architectures, data pipelines, and deployment guidelines
• Drive continuous improvement of AI delivery, automation, and operational efficiency

Technical Skills (By Category) Programming Languages (Essential & Preferred)

• Essential: Python
• Preferred: Additional languages such as R, Java, or Go for integration or specialized workloads

AI Frameworks & Libraries

• Essential: TensorFlow, PyTorch
• Preferred: Hugging Face transformers, spaCy, LangChain

Model Development & Deployment

• Essential: Training, evaluation, and deployment of AI models; NLP/vision capabilities as applicable
• Preferred: MLOps tooling, model monitoring platforms, bias detection, governance practices

Cloud & Infrastructure

• Essential: Cloud experience with AWS, Azure, or GCP for training, deployment, and monitoring
• Preferred: Multi-cloud deployments and cloud-native AI services

Data Management & Storage

• Essential: Data preprocessing, feature engineering, data validation, and data lineage awareness
• Preferred: Data governance concepts, data quality frameworks, data virtualization

DevOps & MLOps

• Essential: Version control (Git), CI/CD concepts, and basic monitoring for AI pipelines
• Preferred: Containerization (Docker), orchestration (Kubernetes), CI/CD tooling (GitHub Actions, Jenkins)

Security & Compliance

• Essential: Data privacy, secure model serving, and governance for AI deployments
• Preferred: PCI-DSS/HIPAA awareness and advanced security controls for AI

Experience Requirements

• 5–7 years in AI/ML/data science, with hands-on production experience in deploying ML models
• Demonstrated ability to collaborate with cross-functional teams and deliver AI-enabled solutions
• Prior experience in regulated industries or enterprise environments is advantageous
• Alternative pathways: strong project portfolio, certifications in AI/ML, or significant contribution…