Content Strategist (AI & Data Science)

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Job Description

Roles and Responsibilities

Primary Focus (90% of role):

  • Research & Innovation: Conduct in-depth research on emerging GenAI technologies, frameworks, and models (LLMs, diffusion models, multimodal AI, agentic systems, RAG architectures, etc.) and evaluate their applicability to business challenges.
  • Solution Development & Prototyping: Design, develop, and deploy GenAI solutions and proof-of-concepts for various use cases across different product teams and business units using state-of-the-art tools and frameworks.
  • Code Development: Write production-quality Python code for GenAI applications, including prompt engineering pipelines, fine-tuning workflows, API integrations, vector database implementations, and AI agent systems.
  • Experimentation & Benchmarking: Design and execute experiments to evaluate model performance, conduct A/B testing of GenAI approaches, and establish best practices for implementation.
  • Technical Documentation: Develop detailed technical documentation, architectural diagrams, implementation guides, and knowledge repositories for GenAI solutions.
  • Stay Current: Continuously monitor the rapidly evolving GenAI landscape, experiment with new models and techniques, and bring insights from research papers and industry developments into practical applications.

Secondary Focus (10% of role):

  • Content Review & Validation: Review and validate technical accuracy of AI/ML/GenAI educational content, ensuring alignment with current best practices and industry standards.
  • Technical Quality Assurance: Collaborate with SMEs and instructional designers to ensure code examples, technical explanations, and AI concepts are presented accurately and reflect real-world implementation patterns.

Qualifications

    Education:

    • Bachelor's or Master's degree in Computer Science, Engineering (B.E./B.Tech), Data Science, AI/ML, or related technical field
    • Consistent academic record with minimum 65% throughout

    Experience:

    • Minimum 4 years of hands-on experience in Artificial Intelligence, Machine Learning, or Data Science
    • Minimum 2 years of specialized experience working with Generative AI technologies (LLMs, prompt engineering, RAG systems, fine-tuning, GenAI frameworks)
    • Proven track record of building and deploying AI/ML solutions in production environments.

    Technical Skills (Mandatory):

    • Strong expertise in Generative AI concepts and applications: LLMs (GPT, Claude, Llama, Gemini), prompt engineering, fine-tuning, RAG architectures, vector databases (Pinecone, ChromaDB, FAISS), embeddings
    • Advanced proficiency in Python with experience in AI/ML frameworks: LangChain, LlamaIndex, Hugging Face Transformers, OpenAI API, Anthropic API
    • Solid foundation in traditional Machine Learning and Deep Learning: neural networks, model training, evaluation metrics, MLOps practices
    • Experience with cloud platforms (AWS, Azure, GCP) and their AI/ML services
    • Familiarity with MLOps tools and practices: model versioning, deployment pipelines, monitoring
    • Strong understanding of data structures, algorithms, and software engineering principles

    Technical Skills (Desirable):

    • Experience with AI agents and autonomous systems frameworks (AutoGPT, LangGraph, CrewAI)
    • Knowledge of model fine-tuning techniques (LoRA, QLoRA, PEFT)
    • Familiarity with multimodal AI and computer vision applications
    • Experience with evaluation frameworks and benchmarking methodologies
    • Contributions to open-source AI projects or published research

    Professional Competencies:

    • Exceptional problem-solving abilities with a research-oriented mindset
    • Strong coding skills with ability to write clean, maintainable, and well-documented code
    • Excellent written and verbal communication skills – ability to explain complex technical concepts to both technical and non-technical audiences
    • Self-driven and proactive approach to learning and staying current with rapidly evolving AI technologies
    • Ability to work independently on ambiguous problems while managing multiple concurrent projects
    • Strong collaboration skills to work effectively with cross-functional teams including product, engineering, and business stakeholders
    • Experience presenting technical findings and recommendations to leadership and decision-makers
    • Adaptability and comfort with fast-paced, innovation-driven environments