Generative AI Engineer | Python, Large Language Models & Cloud Deployment

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

• 510 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

• Bachelors or Masters 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

SYNECHRONS 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 applicants gender gender identity sexual orientation race ethnicity disabled or veteran status or any other characteristic protected by law.

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