Generative AI Engineer

Job Summary
Synechron is seeking a highly experienced AI Technical Lead specializing in Generative AI to guide the development and deployment of advanced AI-powered solutions. 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 leverage extensive expertise in Python, ML frameworks, cloud platforms, and MLOps practices to support enterprise AI initiatives that drive innovation, operational efficiency, and strategic growth.

Software Requirements
 

Required Software Proficiency:

• Python (latest stable version, e.g., Python 3.8+) — in-depth experience developing and supporting AI/ML pipelines and automation tasks

• ML Frameworks: PyTorch, TensorFlow — strong hands-on experience in training, fine-tuning, and inference of large models

• Generative AI frameworks: Hugging Face Transformers, LangChain, OpenAI APIs — expertise in developing, prompt engineering, and deploying models

• Cloud Platforms: AWS, Azure, GCP — extensive experience deploying ML models, supporting model lifecycle management in cloud environments

• Model Management & Orchestration: MLflow, Kubeflow — supporting model versioning, monitoring, and continuous training workflows

• Data handling tools: Pandas, NumPy — for data preparation, feature engineering, and analysis supporting model performance

Preferred Software Skills:

• AI model testing: support for automated model validation, bias detection, and performance evaluation tools

• Integration frameworks: support for REST APIs, gRPC, and other deployment tools supporting AI microservices

• Deployment automation: support for CI/CD pipelines using Jenkins, Azure DevOps, or GitLab supporting automated deployment and retraining

Overall Responsibilities

• Lead the end-to-end development of AI models supporting enterprise use cases like NLP, retrieval-augmented generation (RAG), and multimodal AI solutions

• Build scalable, cloud-enabled AI pipelines supporting training, deployment, and continuous learning cycles

• Collaborate with data scientists, engineering, and product teams to translate business needs into AI solutions supporting operational and strategic goals

• Support model optimization for performance, scalability, and cost efficiency in enterprise environments

• Drive prompt engineering, fine-tuning, and evaluation strategies to enhance model effectiveness and fairness

• Implement model validation, bias mitigation, and compliance with AI ethics standards supporting responsible AI practices

• Automate model deployment and monitor model health, performance, and drift using cloud-native tools supporting MLOps

• Maintain documentation on model architecture, training data, evaluation reports, and operational procedures

Technical Skills (By Category)

• Languages & Frameworks (Essential):

  • Python: core language for model development and automation support

  • ML Frameworks: PyTorch, TensorFlow supporting training and inference workflows

  • Transformers and LangChain supporting large language model deployment

• Model Management & Data Handling:

  • Pandas, NumPy supporting data processing and feature engineering

  • Model versioning: MLflow, Kubeflow supporting deployment and lifecycle management

• Cloud & Infrastructure:

  • AWS, Azure, or GCP (preferred) supporting cloud deployment, scaling, and monitoring

  • Cloud-native ML services supporting large-scale training and inference (preferred)

• Tools & Automation:

  • CI/CD support supporting automated model deployment, validation, and retraining pipelines

  • Support for model explainability, bias detection, and monitoring tools

Experience Requirements

• 7-12 years supporting enterprise AI/ML projects, including large language models and multimodal systems

• Proven experience designing, training, fine-tuning, and deploying scalable AI models supporting enterprise use cases

• Extensive expertise supporting AI model automation, versioning, monitoring, and compliance in cloud environments

• Experience working within regulated industries supporting responsible AI and data governance standards (preferred)

• Demonstrated success collaborating with data scientists, ML engineers, and product teams on enterprise AI solutions

Day-to-Day Activities

• Develop, train, fine-tune, and deploy large language models, diffusion models, and multimodal AI solutions supporting enterprise applications

• Build automated data pipelines supporting training, validation, inference, and retraining for continuous learning

• Collaborate with ML teams and stakeholders to support model deployment, monitoring, and optimization workflows

• Conduct model evaluation, bias mitigation, and performance tuning to enhance fairness and operational quality

• Troubleshoot deployment issues, model drift, and inference latency challenges proactively

• Automate retraining, validation, and model management processes supporting MLOps best practices

• Document model architectures, training datasets, evaluation results, and operational procedures supporting compliance and transparency

Qualifications

• Bachelor’s or Master’s degree in Data Science, Computer Science, or related technical fields

• 7-12 years supporting enterprise AI/ML projects with a focus on large language models and multimodal solutions

• Certifications supporting cloud deployment, MLOps, or AI frameworks (preferred)

• Proven experience deploying secure, scalable, and compliant AI models supporting enterprise data privacy and ethical standards

Professional Competencies

• Strong analytical and troubleshooting skills for complex model training, inference, and deployment issues

• Leadership qualities to guide junior team members and promote best practices in ML lifecycle management

• Clear stakeholder communication skills supporting model validation, compliance, and operational reports

• Adaptability to evolving AI research, cloud services, and responsible AI standards

• Strategic thinking to support scalable, secure, and Fair AI solutions supporting enterprise objectives

• Organizational skills for managing model lifecycle, versioning, retraining, and deployment 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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