AT&T -Gen AI
ECMS REQ ID |
542983 |
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PU |
CMTADM |
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Client Name |
AT&T Services Inc.. |
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Number of Openings |
4 |
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Country |
India |
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Detailed JD (Roles and Responsibilities) |
Role Summary: Own agentic workflows (LangGraph), MCP tools, retrieval/embedding pipelines, and model evaluation. Translate ambiguous problems into robust, measurable solutions with clear documentation and business impact. Key Responsibilities · Build agentic workflows in LangGraph; create reusable templates for multi-tool agents. · Design, implement, and operate MCP servers/tools to expose APIs, data access, and actions. · Apply advanced prompt engineering; maintain a versioned prompt registry with telemetry and A/B tests. · Build embedding pipelines for semantic search/classification/clustering/retrieval; integrate with downstream apps (intent detection, topic modeling, deduplication, ranking). · Apply dimensionality reduction and similarity search (PCA/t-SNE/UMAP; cosine/Euclidean; FAISS/ScaNN). · Build and evaluate ML models (regression, random forest, XGBoost/LightGBM, SVM, Naive Bayes, k-means, hierarchical clustering) with sound diagnostics and inference. · Design experiments (A/B/MVT/DOE) and causal analyses (PSM, causal forests, DiD); translate into actionable insights. · Partner with Full Stack and DevOps on data contracts, latency/SLOs, observability, and deployment. |
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Required Technical Skills: · Python (advanced): FastAPI, async I/O, packaging, pytest; Linux proficiency · GenAI/Agentic: LangGraph (templates/orchestration), MCP servers/tools, LLM tool-use/function calling, retrieval, streaming · LLM Fine-tuning & Eval: SFT/ORPO/DPO; prompt/response evaluation frameworks; guardrails · Embeddings & Vector DBs: TF-IDF, Word2Vec, GloVe, FastText, BERT/SBERT, OpenAI/Azure OpenAI; FAISS, Azure AI Search, Pinecone, ScaNN · Statistics & Causal: hypothesis testing, CIs, bootstrapping, Bayesian basics; feature selection (Lasso/Ridge, RFE); SHAP · Data Eng basics: PySpark, SQL; Azure Databricks, Data Factory, Data Lake; MongoDB/Cosmos DB · Observability: MLflow, Application Insights; model drift detection | ||
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Nice to have: Prompt linting/eval frameworks; graph analytics; vector augmentation; prompt caching · Visualization (Plotly, Seaborn) and stakeholder-ready reporting
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Total Experience |
4+years |
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Relevant Experience |
3+ years |
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Mandatory skills |
Python (advanced): FastAPI, async I/O, packaging, pytest; Linux proficiency · GenAI/Agentic: LangGraph (templates/orchestration), MCP servers/tools, LLM tool-use/function calling, retrieval, streaming |
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Desired skills |
Data Eng basics: PySpark, SQL; Azure Databricks, Data Factory, Data Lake; MongoDB/Cosmos DB |
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Domain (Industry) |
Telecom |
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Work Location |
Bangalore, India |
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Background Check (Before onboarding / After onboarding) |
After Onboarding |
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Mode of Interview- Telephonic/Face to Face/Video Interview |
F2F or Video |
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WFO / WFH / Hybrid |
Hybrid. Mon-Wed need to work from office |
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Shift Details (Time) |
12.30PM to 9.30PM |
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Vendor Rate (INR/day) |
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11,500 INR/Day to 12,500 INR/Day |
