Senior AI Engineer

Senior AI Engineer

Neemtree·full-time

Job descriptionApplication

About the role

Key Responsibilities

• 1. Generative AI & Agent Development

• Design and build LLM-powered applications and AI agents

• Develop workflows using: LangChain / LangGraph / LlamaIndex

• Implement: Function calling / tool usage + Chain-of-Thought / ReAct reasoning

• Build end-to-end agentic systems capable of multi-step reasoning

• 2. RAG (Retrieval-Augmented Generation) Systems

• Design and implement scalable RAG pipelines

• Work with: Vector databases (Pinecone, FAISS, Milvus, etc.)

• Handle: Document chunking strategies

• Embedding generation and retrieval optimization

• Improve response quality and reduce hallucinations

• 3. LLM Optimization & Fine-Tuning

• Fine-tune models using: LoRA / PEFT techniques

• Optimize performance via: Quantization (8-bit / 4-bit)

• Manage: Context window limitations

• Prompt engineering strategies

• Work with alignment techniques like RLHF (understanding level expected)

• 4. Deep Learning & Model Understanding

• Strong grasp of: Neural networks & backpropagation

• Regularization techniques (Dropout)

• Experience with: LSTMs / RNNs (foundational understanding)

• CNNs (for vision-based use cases, if applicable)

• Train and optimize models using GPU/TPU environments

• 5. Machine Learning & MLOps

• Apply ML techniques for: Structured and unstructured data

• Strong understanding of: Class imbalance problems

• Evaluation metrics (Recall, Precision, etc.)

• Work with: XGBoost / Random Forest for tabular problems

• Handle: Model monitoring

• Data drift / concept drift detection

• Build production-ready ML pipelines

• Required Skills & Experience

• Must-Have

• 2–5 years of experience in AI / ML / GenAI engineering

• Strong hands-on experience with: Python + LLM frameworks (LangChain, LlamaIndex, etc.)

• Experience building: RAG pipelines + AI agents / tool-using systems

• Understanding of: Vector databases + Prompt engineering & hallucination mitigation

• Solid ML/DL fundamentals

• Good to Have

• Experience with: Fine-tuning LLMs (LoRA/PEFT) + Quantization & optimization techniques + GPU-based model training

• Exposure to: MLOps tools & deployment pipelines + Real-time AI applications

• Must-have skills

• Artificial IntelligenceMachine LearningPython

• Good-to-have skills

• LLMRAG

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