We are currently hiring for GenAI developer role.
Generative AI Developer / Engineer / Analyst
Total Industry Experience should be 5+ years
- 2-3 years of hands‑on experience working with Generative AI technologies
- 23 years of experience in data analysis, business analysis, or analytics‑heavy roles
Role Summary
We are seeking a Generative AI Developer/Analyst who combines strong data and analytical thinking with the ability to effectively design, test, and optimize LLM‑powered solutions. While traditional programming skills are important, this role places a strong emphasis on data understanding, prompt engineering, and written communication, as English is effectively the “programming language” of GenAI systems.
The ideal candidate is a technologist first, comfortable coding in any modern language, and equally comfortable working with data, requirements, and stakeholders.
Key Responsibilities
Generative AI Development
- Design, build, and optimize applications using Large Language Models (LLMs) such as LLama, GPT, Claude, Gemini, or similar
- Develop and refine prompt engineering strategies to improve accuracy, reliability, and consistency of GenAI outputs
- Implement prompt versioning, evaluation, and experimentation frameworks
- Apply techniques such as few‑shot learning, chain‑of‑thought prompting, RAG (Retrieval‑Augmented Generation), and tool/function calling
Data & Analysis
- Analyze structured and unstructured data to determine suitability for GenAI use cases
- Work closely with business stakeholders to translate requirements into LLM‑ready inputs and workflows
- Perform data profiling, cleansing, and transformation to support GenAI pipelines
- Evaluate model outputs using qualitative and quantitative metrics
Engineering & Integration
- Implement GenAI solutions using any modern programming language (e.g., Python, Java, JavaScript, Go, etc.)
- Integrate LLMs with APIs, databases, vector stores, and enterprise systems
- Build scalable and secure GenAI services following engineering best practices
- Collaborate with DevOps and cloud teams for deployment and monitoring
Communication & Documentation
- Produce clear, well‑structured written documentation (prompts, design docs, evaluation criteria)
- Communicate complex AI behavior and limitations to non‑technical stakeholders
- Maintain prompt libraries, usage guidelines, and best practices
Required Skills & Qualifications
Core Requirements
- 2–3 years experience working with Generative AI / LLM‑based systems
- 1–3 years hands‑on experience with prompt engineering
- Strong written English skills with the ability to craft precise, unambiguous instructions
- Strong data analysis or business analysis background (2–3 years)
- Ability to code in at least one programming language (language‑agnostic mindset)
Solid Understanding of
- LLM behavior, strengths, and limitations
- Tokenization, context windows, and cost/performance trade‑offs
- Hallucinations, bias, and output validation techniques
Technical Knowledge
Experience with
- OpenAI / Azure OpenAI / Anthropic / Google GenAI APIs
- Vector databases (e.g., Pinecone, FAISS, Weaviate, OpenSearch)
- RAG architectures
Familiarity with
- APIs and microservices
- SQL and/or NoSQL databases
- Cloud platforms (AWS, Azure, or GCP)
Preferred / Nice‑to‑Have Skills
- Experience building enterprise GenAI solutions
- Knowledge of evaluation frameworks for LLM outputs
- Experience with document processing, summarization, or contract analysis
- Understanding of data privacy, security, and compliance in AI systems
- Exposure to MLOps / LLMOps concepts
Mindset & Traits
- Strong analytical and problem‑solving skills
- Curious, experimental, and comfortable with ambiguity
- Ability to think in language, logic, and data simultaneously
- Business‑oriented mindset with attention to real‑world impact
Why This Role Is Different
- GenAI development is not only about coding
- English is a core development skill
- Success depends on data understanding, prompt design, and analysis, as much as software engineering