AI Engineer
Job Description
Key Skills Required
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The Role
We are looking for a highly skilled AI Engineer with 7+ years of experience in software engineering, with a heavy focus on Python, AWS infrastructure, and Generative AI.
The ideal candidate will be responsible for building high-performance API services and implementing complex RAG and Agentic AI architectures.
Requirements
Key Requirements:
- Experience: Minimum of 7+ years of professional experience in software development and AI engineering.
- Generative AI Integration: Hands-on experience integrating Generative AI/LLM APIs, AWS Bedrock, and other model providers.
- Infrastructure & DevOps: Experience with DevOps, CI/CD pipelines, and ML pipelines within the AWS ecosystem.
- Agentic AI: Exposure to building Gen AI/Agentic AI applications, managing efficiency, latency, and backend infrastructure.
- Technical Standards: Strong Python programming skills with a deep understanding of OpenAI API standards, JSON RESTful design, and LLM orchestration.
- Preferred Skills: Experience working with Bedrock Agent/Core services is a significant plus.
Candidates will be expected to demonstrate deep technical proficiency in the following areas:
1. Retrieval-Augmented Generation (RAG)
- Ability to design and implement end-to-end RAG pipelines, including retrievers, vector stores (e.g., Pinecone, Weaviate, or pgvector), and generators.
- Expertise in latency optimization and relevance tuning to ensure production-grade performance.
- Strategic approach to document chunking and embedding, balancing granularity with semantic coherence.
2. Agent Development
- Practical experience developing autonomous or semi-autonomous agents using frameworks such as LangChain, CrewAI, or Semantic Kernel.
- Ability to manage orchestration, tool integration, and robust error handling for non-deterministic AI outputs.
- Proficiency in managing memory and context (episodic vs. long-term) in multi-turn interactions and external API interfacing.
3. Evaluation and Optimization
- Familiarity with evaluation frameworks (e.g., RAGAS, TruLens) to assess performance, grounding accuracy, and hallucination detection.
- Ability to iterate systems based on performance metrics and continuous improvement practices.
Benefits
Significant career development opportunities exist as the company grows. The position offers a unique opportunity to be part of a small, fast-growing, challenging and entrepreneurial environment, with a high degree of individual responsibility.
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Tiger Analytics
View Company ProfileTiger Analytics is a global leader in AI, data engineering, and advanced analytics consulting. The company partners with Fortune 500 enterprises to build end-to-end AI and machine learning solutions, custom data platforms, and predictive analytics tools that drive strategic business transformation across retail, financial services, healthcare, and manufacturing.
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