ML Engineer

Experience

: 3.00 + years

Salary

: Confidential (based on experience)

Expected Notice Period

: 15 Days

Shift

: (GMT+05:30) Asia/Kolkata (IST)

Opportunity Type

: Remote

Placement Type

: Full Time Permanent position(Payroll and Compliance to be managed by: Zero to 1)

(*Note: This is a requirement for one of Uplers' client - Zero to 1)What do you need for this opportunity?Must have skills required:Languages: Python 3.10+, LLM, rag, Classical ML: scikit-learn, Feature Engineering, LightGBM/XGBoost, MLOps, Model evaluation, Pandas/numpy, Recommendation System, Cloud Server (Google / AWS), SQLZero to 1 is Looking for:

Context for candidates

• RecSys v1: Embedding-based candidate gen from Milvus + L2R (LightGBM) re-ranker with business rules
• RAG quality: retrieval metrics, hard-negative mining, prompt & grounding eval suites
• Agent signals: convert agent outputs (rationales/paths) to features; add auditability & explanations
• Data contracts: Bronze/Silver/Gold tables on Delta with provenance columns
  

Must-have keywords (for resume screening)

• Languages: Python 3.10+, strong SQL
• Data: pandas, NumPy, PyArrow, Delta Lake (S3), Parquet; Spark/EMR Serverless or Glue (nice)
• Classical ML: scikit-learn, LightGBM/XGBoost, feature engineering, model selection, cross-val
• Recommenders: candidate generation (embeddings/similarity), learning-to-rank, click models, cold-start strategies
• LLMs & Agents: prompt design, tool/function calling, RAG, retrieval evaluation, LangChain/LangGraph
• Embeddings & Vector Search: text embedding models, Milvus/Zilliz (IVF/HNSW),FAISS basics
• Evaluation: NDCG@K, MRR, MAP, precision/recall, AUC; offline/online A/B test design, guardrails
• MLOps/Experimentation: MLflow or Weights & Biases, Docker, reproducible notebooks → scripts, data/label versioning
• APIs: package a model via FastAPI (basic), pydantic, async I/O familiarity
• Security/tenancy: PII handling, anonymization, KMS/IAM basics
  

Nice-to-have

• Graph/GraphRAG: Neo4j, graph features (PPR, Jaccard) for constrained recommendations
• Re-rankers: cross-encoders, reranking with transformers
• Causal/Experimentation: CUPED, diff-in-diff, power analysis
• Batch & Orchestration: Step Functions/EventBridge, Airbyte/dbt
• Observability: data quality (Great Expectations), tracing/metrics in CloudWatch
• Bedrock/OpenAI: model selection, cost/latency tradeoffs, prompt safety
  

Day-to-day toolset

• Python (poetry/uv), Jupyter/VS Code, GitHub Actions CI
• Data: Athena/Glue Catalog, S3 + Delta, SQL on Postgres/Aurora for app joins
• Vector: Milvus (HNSW/IVF), embedding pipelines
• Serving: FastAPI endpoints for ranking/feature-service; SQS/Step Functions for batch jobs
• Experimentation: MLflow model registry, W&B dashboards, AB testing framework hooks
  

How to apply for this opportunity?

• Step 1: Click On Apply! And Register or Login on our portal.
• Step 2: Complete the Screening Form & Upload updated Resume
• Step 3: Increase your chances to get shortlisted & meet the client for the Interview!
  

About Uplers:

Our goal is to make hiring reliable, simple, and fast. Our role will be to help all our talents find and apply for relevant contractual onsite opportunities and progress in their career. We will support any grievances or challenges you may face during the engagement.(Note: There are many more opportunities apart from this on the portal. Depending on the assessments you clear, you can apply for them as well).So, if you are ready for a new challenge, a great work environment, and an opportunity to take your career to the next level, don't hesitate to apply today. We are waiting for you!