AI Engineer

Key Responsibilities
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Build and deploy AI-powered applicationsusing LLMs (OpenAI, Azure OpenAI, etc.)
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Design and implement advanced prompt engineering techniques:
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Few-shot / zero-shot prompting
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Chain-of-thought prompting
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Prompt optimization & evaluation
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Develop RAG-based solutionsusing embeddings and vector databases
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Integrate AI models into APIs, products, and business workflows
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Build lightweight ML models such as:
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Recommendation systems
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Classification/regression models (Random Forest, XGBoost, etc.)
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Perform basic model training, evaluation, and tuning
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Work with tools like LangChain / LlamaIndex / Semantic Kernel /n8n
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Optimize model performance, response quality, cost, and latency
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Rapidly prototype and deliver POCs (2–4 weeks)
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Collaborate with business and engineering teams to solve real problems
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Continuously experiment with emerging AI tools and techniques
Required Skills
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Strong Pythonskills (must-have)
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Strong Prompt Engineering
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Ability to design, test, and refine prompts for accuracy and reliability
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Understanding of hallucination control and output structuring
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Experience working with LLMs, embeddings, and RAG
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Basic understanding of ML concepts:
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Model training, validation, overfitting
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Hands-on with at least one algorithm (Random Forest / regression etc.)
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Experience with APIs / backend integration
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Familiarity with:
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Vector DBs (FAISS / Pinecone / Chroma)
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Prompt testing and evaluation approaches
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Knowledge of cloud platforms(Azure preferred)
Junior AI Engineer
14 May 2026
15:41
Rough Page 1
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Knowledge of cloud platforms(Azure preferred)
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Strong problem-solving + fast learning mindset
Good to Have
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Exposure to LangChain / LlamaIndex
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Experience building chatbots / copilots / AI assistants
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Basic understanding of MLOps / deployment pipelines
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Familiarity with Docker / API deployment
What We Are Looking For
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Fast learner > years of experience
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Strong logical thinking & curiosity
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Ownership-driven and proactive
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Ability to translate ambiguous business problems into AI solutions
KPIs / Success Metrics
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High-quality prompt design leading to accurate AI outputs
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POCs delivered within 2–4 weeks
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Working AI applications in production
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Optimized LLM cost vs performance
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Ability to independently learn and apply new AI techniques
Rough Page