Gen AI Architect

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Job Description

Must Have Technical/Functional Skills

Be an expert source on machine learning to drive delivery of new and innovative solutions. Propose creative solutions to approach business solutions with emerging technologies. Prototype new ways of applying technologies for solving business problems. Educate others so that they can demonstrate the innovative methods for achieving outcomes. Build and maintain machine learning principles, best practices, and code accelerators. Conduct external research and internal experimentation for machine learning techniques. Champion solution delivery behaviors and approaches from software engineers that accelerate delivery of reliable solutions and create a culture of teamwork.Analyze and communicate strategy, status, and product roadmaps to multiple audiences, including all levels of management.

Roles & Responsibilities

GenAI Application Development Expertise Programming Languages: Python Development Tools: LangChain, LlamaIndex, LangFlow, Langgraph, LangSmith, Flowise Techniques: RAG Techniques Databases: Vector Databases (Pinecone, Weaviate, Qdrant) Additional Technologies: Knowledge Graphs, FastAPI, Streamlit, Gradio 2. Domain Model Fine-Tuning Capabilities Languages & Libraries: Python, Data Engineering, OSS LLMs (Llama2, Mixtral, GPT-Neo, GPT-J) Tokenization & Frameworks: Tokenizers (SentencePiece, Hugging Face Tokenizers), Fine-tuning Frameworks (Hugging Face Transformers, PyTorch Lightning) Datasets: HuggingFace Datasets, TensorFlow Datasets 3. LLMOps Proficiency Infrastructure & CI/CD: DevOps, Kubernetes, Docker, Git, Jenkins, GitLab, GitHub Actions Monitoring & Management: Ray, SeldonCore, MLFlow, MLServer, Triton, BentoML, Prometheus, Grafana 4. Data Engineering for AI Applications Data Processing & Management: Python, Apache Spark, Apache Kafka, AWS S3, Azure Data Lake Storage (ADLS), Delta Lake Workflow Automation: Apache Airflow, dbt, Apache NiFi, Fivetran, Airbyte, Great Expectations Data Catalogs: Amundsen, Collibra, Alation 5. AI-Ready Cybersecurity Knowledge Threat Modeling & Security: AI-Specific Threat Modeling Tools, Secure ML Pipeline Tools, API Security Tools Monitoring & Prevention: AI Security Monitoring Tools, Prompt Injection Prevention Libraries, Adversarial Example Detection Libraries 6. GenAI Guardrails and Ethics Ethics & Fairness: AI Ethics Frameworks and Tools, Bias Detection and Mitigation Tools, Fairness Metrics Libraries Privacy & Security: Privacy-Preserving Machine Learning Libraries, Robustness and Security Tools Transparency & Governance: Model Interpretability Libraries, AI Governance Frameworks and Tools Basic Qualifications Bachelor’s degree in computer science, Data Science, or a related field; master’s degree preferred. 5+ years of professional and/or postgraduate academic research experience in software engineering. Preferred experience in SAP / Salesforce or Oracle programs. 4+ year of experience designing and developing machine learning solutions. 3+ years of experience with cloud native engineering, AWS, Azure, Google.

Generic Managerial Skills, If any

Lead a team of junior developers Ability to translate requirements into understandable technical design. Task effort estimation and distribution among team members. Excellent communication skills, and stakeholder management. Ability to work with Cross functional teams and business users.