Artificial Intelligence Developer

Generative AI (GENAI) Developer

Generative AI (GenAI) Developer

Responsibilities:

1. AI Solution Development

• Build and deploy GenAI applications using LLMs (e.g., OpenAI, Anthropic, Google Gemini, Llama).
• Design and optimize prompts, retrieval-augmented generation (RAG) pipelines, and model fine-tuning workflows.
• Develop intelligent agents, chatbots, summarization engines, document automation tools, and other AI-enhanced experiences.
• Implement multimodal solutions (text, code, images, speech) when required.

2. Data Engineering & RAG Pipelines

• Construct end-to-end RAG architectures using vector databases (Pinecone, Chroma, FAISS, Elasticsearch).
• Build ingestion pipelines for unstructured data (PDFs, text, images, emails, enterprise documents).
• Ensure data quality, embedding strategy, chunking methods, and metadata design.

3. Model Integration & MLOps

• Integrate AI models into scalable backend systems via APIs and SDKs.
• Implement model evaluation, monitoring, and continuous improvement workflows.
• Apply secure coding, responsible AI, and governance standards.

4. Cloud & Deployment

• Deploy AI services on Azure, AWS, or GCP using managed AI offerings.
• Optimize models for cost, latency, and reliability.
• Build containerized applications (Docker, Kubernetes) as needed.

5. Collaboration & Stakeholder Management

• Partner with product managers, designers, and business teams to identify AI use cases.
• Translate business requirements into technical designs and prototypes.
• Document system architecture, decisions, and best practices.

Other Requirements:

Technical Skills

• Strong proficiency in Python, REST APIs, and cloud-native development.
• Hands-on experience with LLM frameworks (LangChain, LlamaIndex, DSPy, Rasa).
• Knowledge of transformers, fine-tuning (LoRA, PEFT), and vector databases.
• Experience with DevOps/MLOps tools (GitHub Actions, MLflow, Docker, Kubernetes).
• Familiarity with front-end or full-stack development (optional but valuable).

AI & ML Expertise

• Deep understanding of NLP, embeddings, generative models, and evaluation metrics.
• Experience with retrieval, grounding, hallucination mitigation, and prompt optimization.
• Ability to design secure, ethical, and compliant AI systems.

Soft Skills

• Strong problem-solving and analytical thinking.
• Ability to explain complex AI concepts to non-technical stakeholders.
• Collaborative mindset and curiosity to explore cutting-edge AI capabilities.

Qualifications:

• Bachelors or Master’s degree in Computer Science, Engineering, AI/ML, Data Science, or related field.
• Experience with enterprise AI platforms (Azure OpenAI, AWS Bedrock, Google Vertex AI).
• Prior exposure to environments, digital transformation programs, or enterprise-grade AI delivery.

Experience with multimodal models (vision