Building an Offshore AI Team: From LLM Fine-Tuning to Production ML Pipelines

The AI talent crisis

An ML engineer in San Francisco costs $250K-$350K. A senior AI researcher exceeds $400K. These are not theoretical numbers — they are what companies are paying today for talent that barely exists in the US market. There are more open AI/ML positions than qualified candidates in North America.

India's AI talent pool has exploded. IITs, IISc, and IIIT Hyderabad produce world-class ML graduates who publish at NeurIPS, ICML, and ACL. Companies like Google, Microsoft, and Amazon run major AI research labs in India. The downstream talent — engineers who can take research and build production systems — is deep and growing.

What an offshore AI team looks like

Core ML engineering team

• LLM/NLP Engineer: Fine-tunes foundation models (GPT, Llama, Mistral), builds RAG pipelines, implements prompt engineering frameworks. $5,000-$8,000/mo offshore.
• ML Engineer: Builds and deploys traditional ML models — classification, regression, recommendation systems, time series forecasting. Owns MLOps pipeline. $4,000-$6,500/mo.
• Data Scientist: Exploratory analysis, feature engineering, experiment design, and A/B testing frameworks. $3,500-$5,500/mo.
• ML Platform Engineer: Builds the infrastructure — model serving (SageMaker, Vertex AI), feature stores, experiment tracking (MLflow, Weights & Biases). $4,500-$7,000/mo.

Supporting roles

• Data Engineer: Builds the data pipelines that feed ML models. Without clean, reliable data pipelines, your AI team cannot function.
• Frontend Engineer: Builds the user interface for AI-powered features — chatbots, recommendation widgets, search interfaces.

Production ML: where most teams fail

Building a model in a Jupyter notebook is the easy part. Getting that model into production — with proper monitoring, retraining pipelines, and drift detection — is where most teams struggle.

The MLOps stack your team should own

• Training: AWS SageMaker, Google Vertex AI, or Azure ML for managed training infrastructure
• Experiment tracking: MLflow or Weights & Biases for reproducible experiments
• Feature store: Feast for online/offline feature serving
• Model serving: TensorFlow Serving, Triton, or vLLM for LLM inference
• Monitoring: Evidently AI or Arize for data drift and model performance monitoring

The key hire is your ML Platform Engineer. This person bridges the gap between data science notebooks and production systems. They should have strong Kubernetes skills and experience with at least one major ML platform.

LLM-specific considerations

If your AI use case involves large language models, your offshore team needs specific skills:

• RAG architecture: Vector databases (Pinecone, Weaviate, pgvector), chunking strategies, retrieval pipelines
• Fine-tuning: LoRA/QLoRA for efficient fine-tuning on domain-specific data
• Prompt engineering: Structured prompting, chain-of-thought, few-shot learning
• Evaluation: Building evaluation frameworks beyond vibes — BLEU, ROUGE, human preference scoring

Check our AI/LLM interview questions and salary guide for detailed hiring benchmarks.