GenAI Engineering
Job Summary
Synechron is seeking a seasoned Gen AI Engineer to lead the development and deployment of agentic AI solutions supporting enterprise business processes. This role involves designing, fine-tuning, and implementing large language models (LLMs), retrieval-augmented generation (RAG) systems, and multimodal agents, with a focus on delivery, performance, and operational security. The ideal candidate will leverage extensive experience in Python, cloud platforms, and AI frameworks, collaborating across teams to innovate and provide scalable, secure AI solutions that support business growth.
Software Requirements
Required Software Proficiency:
Python (latest stable version, e.g., Python 3.8+) - extensive hands-on experience supporting training, fine-tuning, and inference of large AI models (supporting 5-10 years)
AI Frameworks: PyTorch, TensorFlow - proven expertise in training, deploying, and optimizing deep learning models supporting generative and multimodal capabilities
Large Language Models: GPT, Claude, Llama, Gemini, or similar - experienced in prompt engineering, fine-tuning, and deployment support (supporting 3+ years)
Cloud Platforms: AWS, Azure, or GCP - experience deploying and managing scalable AI models supporting enterprise solutions (preferred support, 3+ years)
Model orchestration & management: MLflow, Kubeflow supporting model lifecycle, versioning, and monitoring (preferred support)
Data processing: Pandas, NumPy supporting data preparation and feature engineering support
Preferred Software Skills:
AI model evaluation and bias mitigation tools supporting model fairness and performance assessment
MLOps pipelines supporting continuous deployment, retraining, and automation support (Kubeflow, TFX, or similar)
Multi-modal processing frameworks supporting text, images, and audio inputs (preferred)
Overall Responsibilities
Lead the design, training, and deployment of large language models and multimodal agents supporting enterprise automation and insights
Develop scalable AI pipelines supporting real-time inference, retraining, and model monitoring in cloud environments
Collaborate with data scientists, platform engineers, and business stakeholders to translate use cases into operational AI systems supporting automation and decision support
Support prompt engineering, model evaluation, bias detection, and performance tuning for operational reliability and fairness
Automate deployment, versioning, and monitoring workflows supporting MLOps and responsible AI standards
Conduct model validation, interpretability checks, and security assessments supporting compliance in regulated environments
Support enterprise data pipelines supporting multimodal, retrieval-augmented, and knowledge-based AI systems supporting operational transparency
Document model architecture, training, tuning, deployment procedures, and operational metrics supporting audit and compliance regimes
Technical Skills (By Category)
Languages & Frameworks (Essential):
Python supporting large-scale model training, fine-tuning, and scripting for automation
PyTorch and TensorFlow supporting deep learning model development and deployment
Supporting libraries: Hugging Face Transformers, LangChain, support for RAG architecture and plugin integration
Data & Model Management:
Pandas, NumPy supporting data preparation, feature engineering, and validation
Model versioning tools: MLflow, Kubeflow supporting lifecycle management and deployment support
Cloud & Infrastructure:
AWS, Azure, or GCP supporting scalable deployment and inference in enterprise settings (preferred)
Container orchestration support: Docker, Kubernetes supporting scalable, cloud-native AI systems
Model Evaluation & Monitoring:
Tools supporting bias detection, fairness assessment, and inference monitoring (e.g., TensorBoard, custom dashboards)
Experience Requirements
4+ years supporting enterprise AI/ML projects, including large language models, retrieval-augmented generation, and multimodal systems
Proven experience in deploying AI models supporting automation, knowledge management, and operational workflows
Extensive hands-on expertise in cloud AI deployment, orchestrating model lifecycle, and scalable inference support (preferably in regulated environments)
Experience supporting responsible AI practices, model fairness, and security in enterprise settings
Day-to-Day Activities
Develop, fine-tune, and deploy large language models and multimodal agents supporting enterprise automation workflows
Build and automate AI pipelines supporting training, inference, retraining, and model monitoring workflows in a cloud environment
Collaborate closely with data scientists, platform teams, and business units to deliver scalable AI solutions supporting operational efficiency
Conduct bias, fairness, and security evaluations supporting compliance and trustworthy AI practices
Troubleshoot and optimize model inference latency, retraining workflows, and deployment environments supporting enterprise scale
Automate model deployment, monitoring, and retraining pipelines supporting continuous delivery and performance tuning
Document AI architecture, models, training, and operational procedures supporting audit readiness and governance
Qualifications
Bachelor’s or Master’s degree in Data Science, Computer Science, AI, or related technical fields
4+ years supporting enterprise AI/ML solutions, including large language models, retrieval-augmented systems, and multimodal agents
Certifications supporting cloud platforms (AWS, GCP, Azure) or responsible AI practices are advantageous (preferred)
Proven experience supporting or leading compliant, scalable AI systems supporting data privacy and fairness standards
Professional Competencies
Strong analytical and troubleshooting skills supporting complex model validation, bias detection, and model performance issues
Ready to work in hybrid model , for 3 days a week at client office.
Leadership qualities to guide and mentor junior AI engineers and promote best practices in responsible AI deployment
Stakeholder communication skills supporting requirement translation, reporting, and compliance documentation
Adaptability supporting evolving AI research, cloud services, security, and regulatory standards
Strategic thinking supporting scalable, secure, and ethical AI systems supporting enterprise objectives
Organisational skills to manage multiple models, retraining cycles, and deployment activities efficiently in complex environments
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.