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Mid-level LLM role with niche skills but non-Tier1, non-metro location, yielding moderate competition.
Highly ML/LLM specialized role requiring deep model and productionization experience, limiting cross-industry transferability.
Multiple mandatory ML/LLM, deployment, and infrastructure skills plus explicit 3–6 years requirement.
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Design and deploy scalable Retrieval-Augmented Generation (RAG) pipelines and LLM-powered applications with frameworks like LangChain and vector databases such as FAISS or Pinecone.
Build and optimize AI production systems emphasizing reliability, observability (logging, tracing, telemetry), latency, cost efficiency, and robustness against hallucinations and adversarial inputs.
Develop automated evaluation and fine-tuning pipelines using methods like LoRA and implement integration of AI services into production backends/frontends via APIs.
3–6 years of experience in designing and deploying AI/ML systems in production environments.
Advanced proficiency in Python and hands-on experience with PyTorch and/or TensorFlow.
Practical experience with Hugging Face Transformers, RAG pipelines, vector search, and embedding models.
Familiarity with containerization (Docker), Kubernetes deployment, MLOps tooling, and API/microservices design.
Strong production focus with ability to ensure system robustness, scalability, and measurable impact of AI solutions.
Deep reasoning about model behaviors, engineering constraints, and agile iteration while maintaining rigor.
Experience collaborating cross-functionally with backend/frontend teams and translating research-level AI concepts into applied systems.