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Data Engineering Architect - Artificial Intelligence

Data Engineering Architect - Artificial Intelligence

ERAYAHyderabad
5 days ago
Job description

Description :

Location : Hyderabad (Hybrid)

Job Description : Responsibilities :

Technical Leadership & Architecture :

  • Design and architect end-to-end AI and data engineering solutions that meet enterprise-scale requirements
  • Lead the development of proof-of-concepts (POCs) for AI initiatives, ensuring rapid prototyping and validation of technical approaches
  • Establish technical standards, best practices, and architectural patterns for AI and machine learning implementations
  • Evaluate and recommend AI / ML technologies, frameworks, and platforms aligned with client needs and industry trends
  • Oversee data pipeline architecture, ensuring scalable, secure, and efficient data processing workflows
  • Drive MLOps practices implementation, including deployment pipelines, and monitoring systems

Client Engagement & Solutions :

  • Collaborate directly with clients to understand business requirements and translate them into technical AI solutions
  • Present technical proposals, solution architectures, and POC results to client stakeholders and C-level executives
  • Lead technical discovery sessions and requirements gathering workshops with client teams
  • Provide technical consultation on AI strategy, roadmaps, and implementation approaches
  • Ensure the successful delivery of AI projects from conception through production deployment
  • Act as the primary technical point of contact for enterprise AI engagements

    • Team Development & Mentorship :

  • Mentor and guide data scientists, ML engineers, and data engineers in technical best practices
  • Conduct technical training sessions and knowledge transfer workshops for internal teams
  • Review and provide feedback on technical designs, code implementations, and architectural decisions
  • Foster a culture of innovation and continuous learning within the AI practice
  • Identify skill gaps and recommend training programs for team capability enhancement
  • Lead cross-functional collaboration between AI, engineering, and business teams

    • Strategic Initiatives & Management Collaboration :

  • Work closely with senior management to develop and execute AI practice strategy
  • Provide technical insights and recommendations for business development and partnership opportunities
  • Contribute to go-to-market strategies for AI solutions and service offerings
  • Participate in strategic planning sessions and provide technical feasibility assessments
  • Drive innovation initiatives and research partnerships with academic institutions and technology vendors
  • Support pre-sales activities with technical expertise and solution positioning

    • Industry Representation & Thought Leadership :

  • Represent Company at AI industry conferences, technical forums, and professional events
  • Deliver keynote presentations, technical talks, and panel discussions at industry gatherings
  • Author technical white papers, blog posts, and thought leadership content on AI trends and best practices
  • Build and maintain relationships with AI community leaders, researchers, and technology vendors
  • Participate in industry standards committees and AI ethics discussions
  • Monitor and analyze emerging AI technologies and their potential business applications

    • Qualifications :

  • Bachelors or Master's in Computer Science, Data Science, Machine Learning, or related technical field
  • Minimum 10-12 years of experience in data engineering, machine learning, or AI solution development
  • 5+ years in senior technical or architectural roles with client-facing responsibilities
  • Proven track record of leading large-scale AI / ML implementations in enterprise environments
  • Experience with end-to-end ML lifecycle from data collection to model deployment and monitoring
  • Deep expertise in machine learning frameworks (TensorFlow, PyTorch, Scikit-learn, etc.)
  • Strong programming skills in Python, R, Scala, or Java with focus on production-quality code
  • Extensive experience with cloud platforms (AWS, Azure, GCP) and their AI / ML services
  • Proficiency in big data technologies (Spark, Hadoop, Kafka, Databricks, Snowflake)
  • Knowledge of containerization and orchestration tools (Docker, Kubernetes, MLflow)
  • Experience with data pipeline tools (Airflow, Prefect, Azure Data Factory, etc.)
  • Understanding of database systems (SQL, NoSQL, Vector databases, Graph databases)
  • Familiarity with generative AI, LLMs, and foundation model architectures
  • Exceptional communication skills with ability to explain complex technical concepts to non-technical stakeholders
  • Strong presentation and public speaking abilities for client meetings and industry events
  • Demonstrated leadership experience in mentoring technical teams and driving cross-functional initiatives
  • Project management experience with Agile / Scrum methodologies and enterprise delivery frameworks
  • Strong analytical and problem-solving skills with systems thinking approach
  • Experience in consulting or client-services environments with proven client relationship management
  • Industry certifications in cloud AI / ML platforms (AWS ML Specialty, Google Cloud ML Engineer, Azure AI Engineer)
  • Experience with specific industry domains (healthcare, finance, retail, manufacturing) and their AI applications
  • Published research papers, patents, or technical publications in AI / ML field
  • Active participation in open-source AI / ML projects and communities
  • Speaking experience at technical conferences or industry events
  • Knowledge of AI ethics, responsible AI practices, and regulatory compliance (GDPR, CCPA, etc.)
  • Experience with edge computing, IoT, and real-time AI applications
  • Background in computer vision, NLP, or other specialized AI domains

    • Key Performance Indicators :

  • Successful delivery of AI POCs and their transition to production systems
  • Client satisfaction scores and successful renewal of AI engagement contracts
  • Team development metrics including skill advancement and retention rates
  • Industry recognition through speaking engagements, publications, and thought leadership activities
  • Contribution to business growth through technical pre-sales support and solution development
  • Innovation metrics including new solution development and technology adoption rates

    • (ref : hirist.tech)

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