Synechron
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Pune · Hybrid · 1-3 Yrs · Oct 6
Information Technology Support Services · 10000+ Employees · New York, US
We've come a long way We began life in 2001 as a small, self-funded team of technology specialists. Since then, we’ve grown our organization to 14,500+ people, across 58 offices, in 21 countries, in key global markets. Innovative tech solutions for business We're now a leading global digital consulting firm, providing innovative technology solutions for business. As a trusted partner, we're always at the forefront of change as we lead digital optimization and modernization journeys for our clients. Customized end-to-end solutions Our expertise in AI, Consulting, Data, Digital, Cloud & DevOps and Software Engineering, delivers customized, end-to-end solutions that drive business value and growth.
Hiring: Engineering
What employees say
+ Good company with not so good clients.
− They should work more on approaching good clients. Issue with Synechron is you cant change project once you are billable. If they get more clients, employees may have flexibility t
More reviews on the company profile.
Interview experience · Software Engineer
2 rounds, one client round and easy process for onboarding. First is L1 , the. 2 rounds for client followed by HR round. This is the whole interview process. Difficulty of questions depends on role and designation.
Average difficulty · Pune · 705 reports
More interview insightsJob description
Overview
Synechron is seeking a seasoned Gen AI Engineer to lead the development and deployment of agentic AI solutions supporting enterprise business processes.
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
- 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)
- 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
- 5+ 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
- Bachelor’s or Master’s degree in Data Science, Computer Science, AI, or related technical fields
- 5+ 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
- 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
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Job application FAQ
Last updated 13 October 2026
- How do I apply for GenAI Engineer – LLM, RAG, Agentic AI & MLOps at Synechron?
- Open this job on Feedback AI and sign up free (or sign in). You can apply from the job detail page; match scores and skill gaps appear when your profile is complete. Synechron may attach structured assessments to interview rounds after you apply.
- Does Feedback AI show a match score for this GenAI Engineer – LLM, RAG, Agentic AI & MLOps role?
- Yes after you add a resume or skills. Match score ranks this role against your profile — skills met, gaps, level, and location — so you prioritise applications where fit is strongest.
- Is GenAI Engineer – LLM, RAG, Agentic AI & MLOps remote or on-site in Pune, Maharashtra?
- This listing is marked hybrid with location Pune, Maharashtra. Confirm details on the job description before applying.
- What skills matter for this GenAI Engineer – LLM, RAG, Agentic AI & MLOps role at Synechron?
- This is a mid-level GenAI Engineer – LLM, RAG, Agentic AI & MLOps role. Key skills include Hybrid. Feedback AI highlights matched vs missing skills on your profile after signup.