Job Description

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Job Description

We're looking for a junior full stack engineer who is excited about building data-driven and AI-powered applications end-to-end. You'll work across frontend + backend + data + ML integration, helping ship real features such as search, enrichment, classification, and analytics-primarily on Google Cloud Platform (GCP) using a mix of open-source and cloud-native tools.

Day-to-Day Responsibilities

Build and maintain AI-enabled web apps (UI + APIs) that use data and ML/LLM capabilities.

Develop backend services (e.g., FastAPI/Flask/Node) to serve AI predictions, search, and enrichment workflows.

Work with structured and semi-structured data (CSV/JSON/Parquet), write clean SQL, and support data pipelines.

Integrate models and libraries from Hugging Face (Transformers, Datasets, Tokenizers) and common ML tooling.

Implement and test ML inference pipelines (classification, similarity search, reranking, embeddings, evaluation metrics).

Connect systems with GCP services (e.g., BigQuery, GCS, Pub/Sub, Cloud Run), following best practices.

Support vector search / hybrid search integrations where needed (embeddings + keyword search).

Add logging, monitoring, and basic performance improvements (latency, batching, retries).

Write unit tests, document APIs, and collaborate via Git PRs and code reviews.

Typical Tech Stack (What they may touch)

Frontend: React + TypeScript (or similar)

Backend: Python (FastAPI/Flask), REST, background workers

Data: BigQuery, GCS, pandas, SQL, Parquet

AI/ML: scikit-learn, Hugging Face Transformers, embeddings, evaluation tooling

Cloud (GCP): Cloud Run, Pub/Sub, Secret Manager, IAM basics

DevOps: Docker, GitHub Actions/Cloud Build (nice)

We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to HR@insightglobal.com.To learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy:

Skills And Requirements

1-3 years experience

MS/BE in Computer Science, Data Science, Engineering, or equivalent practical experience.

Demonstrable personal/academic projects in AI + data + web apps is highly valued.

Programming & Fundamentals

Strong Python fundamentals (data structures, OOP, debugging, clean code).

Solid understanding of REST APIs, JSON, authentication basics, and integration patterns.

Good working knowledge of SQL (joins, aggregations, window functions are a plus).

Data & ML Basics

Hands-on experience with scikit-learn (training, evaluation, feature basics, metrics like precision/recall/F1).

Familiarity with common ML workflows: train/validate/test, leakage awareness, basic tuning concepts.

Experience working with data using pandas and NumPy.

Hugging Face / LLM & Embeddings Awareness

Basic familiarity with Hugging Face Transformers (loading a model, tokenization, inference).

Awareness of embeddings, semantic similarity, vector search concepts (what they are, why they're used).

Cloud & Deployment (GCP-first)

Exposure to GCP services such as GCS and BigQuery (or willingness to learn quickly).

Ability to containerize and run services using Docker; familiarity with Cloud Run is a strong advantage.

Full Stack / Product Engineering

Experience with at least one modern frontend stack: React (preferred) or Angular/Vue.

Ability to build simple, clean UIs to interact with APIs (forms, tables, filters, pagination).

Engineering Practices

Git workflow (branching, PRs), basic testing mindset, and clear documentation habits. Experience with Vertex AI (model endpoints, embeddings, pipelines) or any managed ML platform.

Familiarity with vector databases and search stacks (FAISS, Elasticsearch/OpenSearch, LanceDB, pgvector, etc.).

Knowledge of hybrid search / reranking (BM25 + embeddings, Cross-Encoder rerankers, evaluation approaches).

Experience with Pub/Sub event-driven patterns and batch/stream processing.

Understanding of MLOps basics: model versioning, reproducibility, experiment tracking (MLflow, W&B).

Basic monitoring/observability: logs/metrics/traces (Datadog, Cloud Monitoring).

Exposure to CI/CD (GitHub Actions, Cloud Build) and infrastructure basics (IAM, service accounts).

Comfort working with Parquet, partitioning strategies, and performance-minded data handling.

Familiarity with LangChain or similar orchestration frameworks (optional).

If you have questions about this posting, please contact support@lensa.com

Job Tags

Full time

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