GenAI Engineer | LLMs, NLP & Cloud (MLOps)

Job Summary
Synechron is seeking a capable and innovative GenAI Engineer to design, develop, and deploy Generative AI solutions across enterprise platforms. This role blends hands-on GenAI, NLP, and data science with cross-functional collaboration to build scalable, secure, and production-ready AI-powered applications. The ideal candidate will advance GenAI initiatives, mentor teammates, and ensure alignment with governance, risk, and compliance requirements while delivering measurable business value.

Software Requirements

Required Skills (Essential)

• Hands-on experience with Generative AI and large language models (LLMs) such as OpenAI, AWS Bedrock, or equivalent

  
  

• Strong proficiency in Python for AI development, model integration, and data processing

  
  

• Expertise with Retrieval-Augmented Generation (RAG) pipelines and serverless/event-driven architectures (e.g., SageMaker + Lambda)

  
  

• Proficiency with vector databases and embeddings (e.g., Faiss, Pinecone)

  
  

• Knowledge of deploying AI models on cloud platforms (AWS, Azure, or GCP) and basic MLOps concepts

  
  

• Experience with AI model governance, data privacy, and security considerations in production

  
  

• Familiarity with version control (Git) and collaborative development workflows

  
  

• Understanding of the SDLC/ML lifecycle, experimentation, and model evaluation

  
  

Preferred

• Containerization (Docker) and orchestration (Kubernetes) for AI services

  
  

• CI/CD pipelines for AI workflows (GitHub Actions, Jenkins, Harness)

  
  

• Model monitoring, bias mitigation, and safety in AI systems

  
  

• Integration of AI solutions with existing enterprise data pipelines and APIs

  
  

• AI tooling for code generation, data labeling, or automated testing

  
  

Overall Responsibilities

• Design, develop, and optimize GenAI/AI-driven solutions and autonomous AI workflows

  
  

• Lead implementation, deployment, and governance of AI models within CI/CD pipelines on cloud platforms

  
  

• Mentor and guide junior AI engineers, promoting best practices in AI development, MLOps, and responsible AI

  
  

• Identify, evaluate, and pilot new AI technologies and architectures to improve business processes

  
  

• Collaborate with product, data, and platform teams to translate business requirements into scalable AI solutions

  
  

• Stay current with AI/ML trends and industry developments; translate insights into actionable plans

  
  

• Ensure governance, risk, and compliance considerations are embedded in AI initiatives

  
  

• Develop and maintain AI architecture, deployment guidelines, and model governance documentation

  
  

• Drive continuous improvement of AI delivery, automation, and operational efficiency

  
  

Technical Skills (By Category)

Programming Languages (Essential & Preferred)

• Essential: Python

  
  

• Preferred: R, Java, or C++ for integration or performance optimization

  
  

AI Frameworks & Libraries

• Essential: PyTorch, TensorFlow, Hugging Face Transformers

  
  

• Preferred: LangChain, SpaCy, OpenAI API usage patterns

  
  

Model Development & Deployment

• Essential: Training, fine-tuning, evaluation, and deployment of LLMs; embeddings and vector-based retrieval

  
  

• Preferred: Encryption layers, secure model serving, model governance practices

  
  

Cloud & Infrastructure

• Essential: Experience deploying AI models on cloud platforms (AWS, Azure, GCP)

  
  

• Preferred: Managed AI services (SageMaker, Vertex AI, Azure ML) and multi-cloud deployments

  
  

Data Management & Storage

• Essential: Vector databases and data pipelines for AI workloads; data preprocessing

  
  

• Preferred: NoSQL databases and data warehousing concepts; data lineage and governance

  
  

DevOps & MLOps

• Essential: Version control (Git), CI/CD concepts, basic monitoring for AI pipelines

  
  

• Preferred: Containerization (Docker), orchestration (Kubernetes), MLOps tools, model monitoring platforms

  
  

Security & Compliance

• Essential: Data privacy, model security, and governance for AI deployments

  
  

• Preferred: AI safety, bias detection/mitigation, auditable model governance

  
  

Experience Requirements

• 4–9 years in AI/ML/Data Science with at least 3 years in GenAI/NLP

  
  

• Proven experience delivering AI/ML solutions in production environments

  
  

• Experience collaborating with cross-functional teams (product, data science, engineering, security)

  
  

• Exposure to regulated industries and governance considerations is a plus

  
  

• Alternative pathways: strong portfolio of GenAI/NLP projects or relevant certifications

  
  

Day-to-Day Activities

• Design, train, fine-tune, and deploy GenAI/NLP models and autonomous AI components

  
  

• Collaborate with product, data, and engineering teams to identify AI use cases and success metrics

  
  

• Build and maintain AI pipelines (training, inference, monitoring) in cloud environments

  
  

• Evaluate new AI techniques, tools, and platforms; lead proofs-of-concept

  
  

• Monitor model performance, detect drift, and implement retraining or adjustments

  
  

• Maintain comprehensive documentation on model architecture, data pipelines, and deployment steps

  
  

• Ensure governance, risk, and compliance considerations are integrated into AI initiatives

  
  

• Mentor junior AI engineers and promote knowledge sharing

  
  

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, AI, or related field

  
  

• 4–9 years of experience in AI/ML/Data Science with at least 3 years in GenAI/NLP

  
  

• Certifications in AI/ML, cloud platforms, or MLOps are advantageous

  
  

Professional Competencies

• Strategic thinking and analytical problem-solving for AI applications

  
  

• Clear communication and stakeholder management for technical and non-technical audiences

  
  

• Leadership and teamwork with ability to mentor peers

  
  

• Adaptability to evolving AI technologies and regulatory landscapes

  
  

• Innovation mindset with a focus on scalable, responsible AI solutions

  
  

• Time management and prioritization in a dynamic, fast-paced environment

  
  

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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