AI/ML Data Engineer | Unstructured Data, PySpark, Python, Vector Search, RAG Architectures, Cloud (GCP/AWS)

Job Summary
Synechron is seeking an experienced AI/ML Data Engineer specialized in processing unstructured data and integrating advanced language models within enterprise environments. This role involves designing scalable data pipelines, implementing document cleansing, classification, and enrichment, and supporting Retrieval-Augmented Generation (RAG) architectures. The successful candidate will bridge data engineering and AI development to enable intelligent, AI-first applications that enhance decision-making and operational efficiency.

Software Requirements

• Required:

  • Strong proficiency in Python (latest stable version) for building data pipelines and implementing ML workflows

  • Hands-on experience with PySpark for distributed data processing and large-scale ETL workflows

  • Experience processing unstructured data such as PDFs, texts, emails, and forms, including OCR and NLP techniques

  • Familiarity with NLP frameworks and libraries such as Transformers, Hugging Face, LangChain, and FAISS

  • Working knowledge of vector databases such as Redis or similar for semantic search and retrieval

  • Understanding of LLM lifecycle management, including fine-tuning, inference, and prompt engineering

  • Experience working with CI/CD practices, Git, and version control tools for data projects

• Preferred:

  • Experience with cloud platforms like GCP, AWS, or Azure, supporting data pipeline deployment

  • Knowledge of data quality metrics and data governance best practices

  • Exposure to data orchestration tools such as Apache Airflow or Prefect

Overall Responsibilities

• Build and maintain scalable, robust data pipelines for unstructured content, ensuring high data quality and performance efficiency

• Develop algorithms for document classification, cleansing, and enrichment to feed AI/ML systems

• Integrate data workflows with LLM pipelines supporting RAG architectures for semantic search and Question-Answering (QA) systems

• Engineer and optimize vector embeddings, document chunking, and metadata tagging for AI applications

• Collaborate closely with AI architects, data scientists, and platform teams to design end-to-end AI solutions

• Implement automation, monitoring, and security best practices to ensure system reliability and compliance

• Support project lifecycle activities, including proof-of-concept, testing, deployment, and ongoing monitoring

• Share domain expertise, conduct knowledge sharing, and mentor team members

Technical Skills (By Category)

• Programming Languages:
  Required: Python, PySpark
  Preferred: SQL, Java, or other scripting languages for automation and integrations

• Databases & Data Management:
  NoSQL (Redis, MongoDB), relational databases (PostgreSQL, MySQL), data tagging, and metadata management

• Cloud Technologies:
  GCP (BigQuery, Dataflow), AWS, or Azure for deployment, scaling, and storage support (preferred)

• Frameworks & Libraries:
  Transformers, Hugging Face, LangChain, FAISS, Spark MLlib, NLP libraries

• Development & Orchestration Tools:
  Git, Jenkins, CI/CD pipelines, Apache Airflow or Prefect (preferred)

• Operational & Security Tools:
  Monitoring platforms (Datadog, Prometheus), security best practices, data encryption

Experience Requirements

• Minimum of 6 years of professional experience in data engineering, with at least 2 years dedicated to unstructured data processing and AI/ML integration

• Proven success building scalable data pipelines supporting NLP, document classification, and semantic search

• Hands-on experience with vector databases, embedding models, and retrieval systems supporting RAG workflows

• Experience working with cloud platforms and performing data quality audits in enterprise environments

• Industry experience in financial services, healthcare, or enterprise AI applications is advantageous

Day-to-Day Activities

• Design, develop, and enhance data pipelines for unstructured data ingestion, processing, and enrichment

• Implement NLP models, document classification, and semantic search capabilities supporting RAG architectures

• Collaborate with data scientists, platform engineers, and stakeholders to address data and AI system needs

• Troubleshoot data pipeline issues, optimize query performance, and implement best practices for data security and governance

• Automate data workflows, manage infrastructure as code, and support cloud deployment strategies

• Monitor pipeline performance, ensure data quality, and document architecture and operational workflows

Qualifications

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

• At least 6 years of experience in data engineering, focusing on unstructured data and AI model integration

• Strong expertise with Python, PySpark, NLP, and vector retrieval systems

• Certifications in cloud platforms or data engineering tools are preferred

• Proven ability to deliver high-quality, scalable, and secure data solutions in enterprise settings

Professional Competencies

• Strong analytical and troubleshooting skills for complex data and AI systems

• Effective communication skills to interface with technical and business stakeholders

• Leadership qualities to mentor team members and promote best practices in data engineering and AI

• Strategic thinking to design scalable, secure, and compliant AI data pipelines

• Adaptability to new tools, frameworks, and emerging AI/ML trends

• Time management skills to prioritize tasks and deliver solutions efficiently

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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