Job Summary
Synechron is seeking an experienced Generative AI Engineer to lead the development and deployment of AI-powered solutions supporting enterprise applications. This role involves designing, fine-tuning, and integrating large language models (LLMs), diffusion models, and transformers into scalable, production-ready systems. The ideal candidate will bring extensive expertise in Python, ML frameworks, cloud platforms, and MLOps tools, contributing to innovative, ethical, and efficient AI solutions that align with organizational goals.
Software Requirements
Required Software Proficiency:
Python (latest stable version, e.g., Python 3.8+) — deep experience in ML pipelines, data processing, and automation
ML Frameworks: PyTorch, TensorFlow — hands-on experience supporting training, fine-tuning, and inference of large models
Generative AI frameworks: Hugging Face Transformers, LangChain, OpenAI APIs — expertise in model development, prompt engineering, and deployment support
Cloud Platforms: AWS, Azure, or GCP — practical experience deploying ML models and supporting CI/CD pipelines in cloud environments
MLOps tools: Docker, Kubernetes, MLflow — for model containerization, orchestration, versioning, and deployment support
Data tools: Pandas, NumPy — experienced in data manipulation supporting model training and evaluation
Preferred Software Skills:
API integration: REST, gRPC support for external data and model interaction (preferred)
Cloud-native services: Support for specialized ML services like AWS SageMaker, GCP Vertex AI (preferred)
Automated testing frameworks supporting model validation and performance testing (e.g., pytest, Model Testing tools)
Overall Responsibilities
Design, develop, and fine-tune large language models, diffusion models, and transformers supporting enterprise AI initiatives
Build scalable data pipelines and automation workflows supporting training, inference, and continuous learning cycles
Collaborate with data scientists, platform engineers, and business stakeholders to translate use cases into operational AI solutions
Support model deployment, versioning, and monitoring using containerization and MLOps practices
Drive innovations in prompt engineering, model optimization, and AI ethics aligned with industry standards (e.g., fairness, transparency)
Implement model validation, performance evaluation, and security practices to ensure compliance and operational safety
Stay current with emerging AI research, frameworks, and cloud services, recommending improvements and new features
Document model architecture, training processes, deployment procedures, and operational metrics
Technical Skills (By Category)
Languages & Frameworks (Essential):
Python: core language supporting ML pipelines, automation, and scripting
PyTorch, TensorFlow: deep learning frameworks supporting training and inference
Transformers, LangChain, OpenAI APIs: model development, prompt engineering, and API-based integrations supporting enterprise solutions
Data & Model Management:
Data manipulation with Pandas, NumPy supporting training data setup and performance tuning
Model versioning, artifact management supporting continuous deployment (MLflow, Model Registry)
Cloud & Infrastructure:
AWS, Azure, or GCP supporting scalable deployment of AI models (preferred)
Cloud-native ML services support supporting large-scale training and inference (preferred)
Tools & Platforms:
Docker, Kubernetes supporting containerized model deployment
CI/CD pipelines supporting automated testing, deployment, and performance monitoring in cloud environments
Security & Governance:
Knowledge of data privacy, model explainability, and fairness standards supporting ethics and compliance
Experience Requirements
5–10 years of professional experience in ML/AI pipeline development, training, and deployment supporting enterprise applications
Hands-on experience with large language models, diffusion models, transformers, and prompt engineering support
Proven expertise in cloud deployment, containerization, and MLOps best practices supporting scalable, service-driven AI solutions
Prior experience supporting AI ethics, model audits, bias mitigation, and compliance in regulated industries (preferred)
Demonstrated success working with cross-functional teams and translating business needs into technical AI solutions
Day-to-Day Activities
Develop and fine-tune large language models, diffusion models, and transformers supporting enterprise application needs
Build and automate ML pipelines supporting training, inference, and model updates using cloud and containerized solutions
Collaborate with data scientists, platform engineers, and business units to deploy, monitor, and improve AI models
Conduct model validation, bias detection, and performance evaluation supporting AI governance and compliance
Troubleshoot model performance issues, optimize inference speed, and ensure scalable deployment
Integrate models with enterprise APIs, external data sources, and business systems supporting operational workflows
Stay updated on AI research, industry best practices, and cloud services, implementing relevant innovations
Document model architecture, training processes, deployment logs, and operational metrics supporting ongoing support and compliance
Qualifications
Bachelor’s or Master’s degree in Data Science, Computer Science, Artificial Intelligence, or related technical fields
5+ years supporting enterprise AI/ML solutions, with experience in training, deployment, and model management supporting large-scale systems
Certifications in Cloud Platforms (AWS, GCP, Azure) or MLOps best practices are a plus
Proven experience deploying secure, compliant, and scalable AI models supporting operational reliability in regulated industries
Professional Competencies
Strong analytical and troubleshooting skills supporting complex model training, optimization, and inference issues
Leadership qualities for guiding model development teams and establishing best practices in AI/ML workflows
Clear stakeholder communication skills for translating AI use cases into technical solutions and operational reports
Adaptability to rapid technological advancements, cloud environments, and responsible AI standards
Strategic thinking to ensure AI models are scalable, secure, and aligned with business and ethical standards
Organizational skills for managing model lifecycle, versioning, validation, and continuous learning workflows
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YNECHRON’S DIVERSITY & INCLUSION STATEMENT
Diversity & Inclusion are fundamental to our culture, and Synechron is proud to be an equal opportunity workplace and is an affirmative action employer. Our Diversity, Equity, and Inclusion (DEI) initiative ‘Same Difference’ is committed to fostering an inclusive culture – promoting equality, diversity and an environment that is respectful to all. We strongly believe that a diverse workforce helps build stronger, successful businesses as a global company. We encourage applicants from across diverse backgrounds, race, ethnicities, religion, age, marital status, gender, sexual orientations, or disabilities to apply. We empower our global workforce by offering flexible workplace arrangements, mentoring, internal mobility, learning and development programs, and more.
All employment decisions at Synechron are based on business needs, job requirements and individual qualifications, without regard to the applicant’s gender, gender identity, sexual orientation, race, ethnicity, disabled or veteran status, or any other characteristic protected by law
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