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

• 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

S​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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