What Employers Are Actually Looking For
Scanning thousands of GenAI job descriptions reveals that employers care much less about credentials and much more about applied skills. The skills that appear most frequently fall into five clusters: LLM APIs and orchestration, RAG and vector databases, fine-tuning and model adaptation, deployment and MLOps, and evaluation.
1. LLM APIs and Orchestration
The ability to work with major model APIs — OpenAI, Anthropic, Google Gemini, Groq — is the baseline. Beyond raw API calls, employers want engineers who can build reliable chains and agents using frameworks like LangChain, LlamaIndex, and AutoGen.
- LangChain: Still the most-mentioned framework in job posts. Know chains, agents, memory modules, and tool-calling patterns.
- LlamaIndex: Preferred for document-heavy RAG use cases. Understand data connectors, query engines, and response synthesisers.
- Structured output: Using Pydantic + LLM function-calling to produce reliable JSON — critical for production systems.
- Streaming: Handling token-by-token responses for real-time UX without breaking your application logic.
2. RAG and Vector Databases
Retrieval-Augmented Generation is mentioned in over 60% of senior AI Engineer postings. It is now a foundational skill, not a differentiator — but depth matters. Surface-level RAG (load document, embed, query) is not enough; employers want engineers who can:
- Implement hybrid search (keyword + semantic) using pgvector, Pinecone, or Weaviate.
- Build re-ranking pipelines to improve retrieval precision.
- Handle chunking strategies for long documents (recursive split, semantic split, parent-document retriever).
- Evaluate RAG quality with metrics like RAGAS faithfulness, context recall, and answer relevance.
3. Fine-Tuning and Model Adaptation
Full fine-tuning of large models is rarely done outside labs, but parameter-efficient methods are widely used in production teams. Skills to highlight:
- LoRA / QLoRA: The go-to techniques for fine-tuning on a single GPU. Know how to set rank, alpha, and target modules.
- Instruction tuning: Preparing datasets in chat or instruct format, managing data quality.
- PEFT library: Hugging Face PEFT is the standard toolchain for parameter-efficient experiments.
- Evaluation after fine-tuning: Measuring task-specific metrics and comparing against the base model to confirm improvement.
4. Deployment and MLOps
The gap between a working prototype and a production system is where many candidates fall short. Employers hiring for senior roles expect familiarity with:
- Containerisation (Docker) and orchestration (Kubernetes) for model serving.
- Inference optimisation — quantisation (GPTQ, AWQ), batching strategies, KV cache management.
- Serving frameworks: vLLM for high-throughput open-weight models, TGI (Text Generation Inference), or FastAPI for lightweight wrappers.
- Monitoring: latency, token usage, hallucination rate, and cost per query dashboards.
- CI/CD pipelines for model versioning and rollback (MLflow, DVC, or Weights & Biases).
5. Evaluation and Safety
As AI products mature, evaluation is becoming a distinct skill set. Employers building customer-facing AI need engineers who can design systematic evaluation pipelines:
- LLM-as-judge frameworks (using a strong model to score another model's outputs).
- Red-teaming: adversarial prompt testing to find failure modes before users do.
- Guardrails: output filtering, content moderation, and schema validation with tools like Guardrails AI or Nemo Guardrails.
- A/B testing model versions with real traffic and business metrics.
Quick-Start Learning Path
If you are starting from a software engineering background and want to build these skills in 90 days:
- Week 1–2: Complete the LangChain and LlamaIndex quickstarts. Build a basic RAG chatbot over your own documents.
- Week 3–4: Add hybrid search with pgvector or Pinecone. Implement re-ranking. Measure quality with RAGAS.
- Week 5–8: Fine-tune a small open model (Llama 3 8B or Mistral 7B) on a specific task using LoRA. Push to Hugging Face Hub.
- Week 9–12: Deploy your RAG system and fine-tuned model. Add monitoring. Write up the whole thing on LinkedIn or a blog.
Three months of focused, project-based learning builds a portfolio that competes with candidates who have years of broad ML experience but no GenAI-specific work.