AI/ML LLM developer

About Codem

Codem is a technology services company specializing in eCommerce, SAP, custom applications, cloud infrastructure, DevOps, and systems integration. We work with global enterprises to build and modernize scalable platforms. We are now seeking a skilled SAP SD Consultant.

ONLY APPLY IF YOU ARE AN IMMEDIATE JOINER. Also you should have passed out of college by 2023 or earlier.

About the role

We’re looking for an AI Developer to build and ship LLM-powered features (chat/search/agents, RAG pipelines, automations). You’ll work closely with product and data teams to turn messy real-world data into reliable, low-latency experiences.

• RAG pipelines:

  Ingest, chunk, embed, and index documents; design retrieval strategies (hybrid/BM25+embedding, metadata filtering, reranking).

• App logic:

  Build APIs/serving layers around LLMs (prompt templates, tool/function calling, agents, streaming).

• Vector DB ops:

  Create/maintain indexes, upserts, namespace/tenant design, TTLs, migrations, and recall/latency tuning.

• Evaluation & quality:

  Set up offline/online evals (accuracy, grounding, toxicity, hallucination rate), A/B tests, and feedback loops.

• Safety & reliability:

  Implement guardrails, prompt-injection defenses, PII redaction, rate limiting, retries, and fallbacks.

• Cost & perf:

  Token budgeting, caching (prompt/result/embedding), batching, and observability for latency & spend.

• Data pipelines:

  Build ETL for PDFs/HTML/docs, enrichment, and scheduled syncs from SaaS/data lakes.

• DevOps/MLOps:

  CI/CD, environment config, secrets management, dataset/version control, and monitoring.

• 3+ years

  software experience (ideally Python) delivering production code.
• Hands-on with

  LLM APIs

  (OpenAI/Azure OpenAI, Anthropic, or local LLMs like Llama) including

  prompting, tools/function calling, and streaming

  .
• Practical

  RAG

  experience using

  vector databases

  (e.g., Pinecone, Weaviate, FAISS, pgvector) and

  embedding models

  .
• Experience with

  LangChain or LlamaIndex

  (or equivalent in-house orchestration).
• Strong with

  web APIs

  (FastAPI/Flask/Node), Git, testing, and debugging.
• Solid understanding of

  security & privacy

  basics (PII handling, secrets, auth).

• Reranking (Cohere/TEI), hybrid search (BM25 + embeddings), or

  Elasticsearch/OpenSearch

  .

• Eval frameworks

  (Ragas, TruLens) and

  telemetry

  (Langfuse, OpenTelemetry).
• Workflow/orchestration (Celery/Temporal/Airflow) and message queues (SQS/Kafka).
• Cloud:

  AWS (Bedrock, Lambda), GCP (Vertex AI), Azure (AOAI)

  , Docker; basic Terraform.
• Frontend collaboration (React) for chat UIs, streaming tokens, and citations.
• Fine-tuning/LoRA, prompt caching, distillation, or model hosting experience.

• Python

  (FastAPI),

  TypeScript/Node

  (optional),

  LangChain/LlamaIndex

• Vector DBs:

  Pinecone, Weaviate, pgvector/FAISS

• LLMs/Embeddings:

  GPT-4/4o/mini, Claude, Llama, instructor/sentence-transformers

• Infra:

  AWS/GCP/Azure, Docker, GitHub Actions, Terraform (basic)

• Obs & Eval:

  Langfuse, Ragas/TruLens, Prometheus/Grafana

• Ship a

  production RAG feature

  with measurable uplift in answer quality.
• Reduce

  latency/cost

  via caching/batching and better retrieval configs.
• Establish

  evaluation + feedback

  loop with clear QA dashboards and guardrails.